Abstract: To augment the inherent efficiency and enhance the quality of the conventional manual beam-tuning methodology, this paper presented an innovative approach through the incorporation of a differential evolution (DE) algorithm. Initially, the architectural framework of the DE algorithm was meticulously delineated, serving as the bedrock of the methodological paradigm. The DE algorithm, renowned for its robust optimization capabilities, is implemented utilizing the versatile Python programming language. This implementation leverages Python’s computational prowess and inherent flexibility, enabling the development of a sophisticated algorithmic solution. A resilient connection with the experimental physics and industrial control system (EPICS) was established via the pyEPICS interface. This integration facilitates seamless communication and precise control between the advanced DE algorithm and the intricate accelerator system. The pyEPICS interface acted as a conduit, ensuring real-time data exchange and enabling dynamic adjustments to be made based on the algorithm’s outputs. Furthermore, to augment user operation and monitoring capabilities, an intuitive control system studio (CSS) interface was devised. This interface empowered efficient upper-level control and real-time monitoring functions, thereby significantly bolstering the usability and practicality of the system. The CSS interface features a user-friendly graphical user interface (GUI) that allows operators to monitor and adjust parameters in real-time with ease, enhancing the overall user experience and operational efficiency. Using the 1.7 MV tandem accelerator platform as a testbed, rigorous experiments were conducted to ascertain the feasibility and efficacy of the DE algorithm in beam optimization. These experiments were designed to comprehensively evaluate the algorithm’s performance under various conditions and constraints. During these trials, this paper not only scrutinized the algorithm’s performance but also implemented optimizations and enhancements based on empirical findings. These refinements notably elevate the optimization capabilities of the algorithm, culminating in an impressive beam transfer efficiency of 80%. The methodology encompassed several pivotal steps. Firstly, the DE algorithm using Python was implemented, capitalizing on its robust computational capabilities and inherent flexibility. This implementation allowed for the development of a sophisticated and adaptable algorithmic solution. Subsequently, the algorithm was seamlessly integrated with the EPICS system via the pyEPICS interface, enabling precise control and monitoring of the accelerator beam. The CSS interface was meticulously developed to offer an intuitive and user-friendly graphical interface, facilitating real-time monitoring and adjustment of parameters by operators. The experimental results underscore that the exceptional performance of the DE algorithm in beam tuning. The optimized beam transfer efficiency of 80% constitutes a substantial improvement over traditional manual methods, highlighting the algorithm’s efficacy in enhancing beam-tuning processes. Furthermore, the DE algorithm’s adaptability and robustness were evident in its proficiency to handle a diverse array of beam conditions and constraints, demonstrating its versatility and practical utility. In conclusion, this study highlights the superior performance of the DE algorithm in beam tuning and proposes a novel approach for the development of intelligent beam-tuning technology. By achieving beam-modulation intelligentization, this paper strives to further enhance the efficiency and stability of accelerator systems. This research not only contributes to the advancement of beam-tuning techniques but also holds considerable promise for related fields of study and practical applications. The findings presented in this paper have the potential to stimulate further research and development in this domain, ultimately culminating in the creation of more efficient and reliable accelerator systems. This work underscores the importance of leveraging advanced algorithmic solutions and robust control systems to enhance the performance and operational efficiency of accelerator facilities.