In the context of demand response at electric vehicle charging stations, user participation has a significant impact on the economic benefits of charging stations. Based on prospect theory, an optimization method for user participation at charging stations is proposed, aiming to improve user participation and economic benefits by altering the composition and format of electricity prices at charging stations. Initially, a user price impact model is established to analyze the influence of charging station prices on electric vehicle users, yielding preliminary user quantity change rates. Subsequently, the value function in prospect theory is used to quantify the decision uncertainty of users when faced with different electricity prices, and adjustments are made to the preliminary user change rates, considering the impact of charging station distance, to obtain the final user quantity changes. Finally, based on the aforementioned models and typical load data from charging stations, with the maximum load during demand response periods as a constraint, the non-dominated sorting genetic algorithm-II (NSGA-II) optimization algorithm is employed to conduct a multi-objective optimization aiming to maximize daily revenue and user participation at the charging station. This determines the composition and format of electricity prices at the charging station, further identifying the optimal user participation and charging station revenue. Simulation results verify the effectiveness of the proposed method.