With the increase of electric vehicles, it’s necessary to continuously improve the energy optimization management strategies in order to give full play to the potential of electric vehicles and achieve the goal of low - carbon economic power supply.The forecasting energy optimization management problem is studied for the electric vehicle charge-exchange-storage integrated pow-er station. Firstly, the Monte Carlo algorithm is used to predict the loads of electric vehicles based on the analysis of the characteristics of the electric vehicle travel chain. Then, aiming at the virtual power plant composed of the electric vehicle charge-exchange-storage integrated power station, interruptible load and distributed power station, a comprehensive objective function using the improved particle swarm optimization algorithm is built to optimize the virtual power station to minimize the operation cost and maximize the economic benefit. In order to determine the optimal charging and discharging power of the electric vehicle charging and storage integrated power station, the coordinated optimal scheduling of exchange power of electrical vehicle, storage and load is carried out. Finally, the distributed energy optimization strategy is applied to an industrial park, and the simulation results verify the effectiveness of the strategy.