With the popularization of electric vehicles, how to efficiently manage their charging behaviors has become a hot topic in the field of energy management. Flexible charging strategies can not only enhance user experience but also play a crucial role in achieving stable grid operation, energy conservation, and emission reduction. This paper first simplifies the target issue of regional power grid through the grid selection method and divides the charging pile area into multiple grids based on the time-load coordinate system to better manage and schedule charging tasks. The study takes into account power level constraints and device-level constraints to ensure that the charging load meets grid limitations and basic user needs. Meanwhile, an optimization function aiming at minimizing the total electricity cost of the charging pile and the peak-to-valley difference of the grid load is proposed. To develop flexible charging strategies and charging plans for different charging models, this paper adopts a genetic algorithm. Through genetic coding and iterative optimization, it derives an electric vehicle charging scheduling scheme that meets grid requirements and user needs. While ensuring efficient operation of charging modules, it achieves balanced loading of modules and dormancy of redundant modules through flexible power allocation, thereby ensuring the long-term stable operation of the charging pile.