There is a configuration optimization problem in the process of integrating electric vehicles and photovoltaic systems into the distribution network and energy storage devices. Thus this paper proposes an energy storage capacity optimization strategy for photovoltaic storage charging stations that considers the orderly charging of electric vehicles. First, it calculates the output power of the photovoltaic system based on a typical daylight intensity curve and the photoelectric energy conversion relationship. Secondly, from charging load influencing factors such as the travel habits of electric vehicle users, charging behavior characteristics, charging mode and so on, a probability model that affects the charging load of electric vehicles is established, and the Monte Carlo method is used to predict the charging load under disorderly charging. Then taking the minimum peak-valley difference of the power grid output curve as the objective function, the particle swarm algorithm is used to calculate the total power grid output load during orderly charging, and it then determines the optimal solution for the energy storage capacity of an optical storage charging station. Finally, the strategy is used to calculate the optimal energy storage capacity in a residential area optical storage charging station with electric private cars and electric taxis as the main service objects. The results show that the disorderly charging of electric vehicles when energy storage is not considered causes the power grid load to add peaks. The peak-to-valley difference of the power grid load is reduced by 15.35% under orderly charging. When the orderly charging of electric vehicles is considered and the optimal energy storage capacity is configured, the peak-to-valley difference of power grid load decreases by 20.65%. This realizes peak shaving and valley filling, and enhances the stability of power system operation. The results obtained in this paper provide a reference for the energy storage capacity configuration of an optical storage charging station. This work is supported by the National Natural Science Foundation of China (No. 51307152).