Research on the operation mechanism of a commercial virtual power plant provides another possible method for a new energy power source integrating into power grids. Integrating wind power, photovoltaic power, an energy storage systems, and an electric vehicle charging station into a virtual power plant can effectively reduce the power grid's stability risk. This is caused by the separate renewable energy output uncertainty and disordered charging of electric vehicles. Adverse impacts on power systems could be alleviated and the consumption of renewable energy could be promoted. The optimal economic benefit of the virtual power plant is taken as the objective, and under the premise of satisfying various constraints, a scheduling optimization strategy is studied. The proposed model is solved by linear and nonlinear inertia weight particle swarm optimization. The results show that the premature convergence at a local optimum can be avoided and higher benefits can be gained with the nonlinear inertia weight particle swarm optimization. A calculated example is carried out to verify the rationality of the model and the effectiveness of the solution. This work is supported by National Natural Science Foundation of China (No. 61761049) and Yunnan Provincial Department of Education Science Research Fund Project (No. 2019Y0169).