With the large-scale network entry of electric vehicles (EVs), their disorder charging further increases the load peak-valley gap, which has a negative impact on the stable operation of power system. A two-stage optimization scheduling strategy which takes into account the EV load and the energy storage system of batteries is proposed. First, an orderly charging scheduling model for EV is established, which aims at minimizing the absolute peak-valley gap between user charging cost and load. The improved particle swarm optimization algorithm is used to solve this model to avoid peak charging. Second, an optimal scheduling model of peak-shaving and valley-filling for the energy storage system is established with an objective of minimizing the variance of load and the combined cost of energy storage life, which is solved by the improved Harris Hawks optimization algorithm to reduce the peak-valley gap of load. In addition, the optimization results are evaluated and analyzed based on the evaluation index of peak-shaving and valley-filling. Finally, a simulation experiment is carried out with the measured load power of one power network as an example. Results show that under the proposed two-stage optimization scheduling strategy, the peak load decreases by about 147 kW, the valley load increases by about 223 kW, and the peak-valley gap deceases by 46.73%, indicating that this strategy can effectively improve the load curve, alleviate the pressure on power supply during the peak load period and ensure the safe and stable operation of power grid.