In order to reduce the risk brought by the uncertainty of renewable energy output and electric vehicle（EV）travel to virtual power plant（VPP）scheduling, an optimal scheduling model of virtual power plant incorporating electric vehicles based on information gap decision theory（IGDT）is proposed. Firstly, a Monte Carlo load prediction model is established based on the behavior characteristics of private EV users, and the Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive price.Secondly, based on the VPP framework, wind power, photovoltaic power, gas turbines, energy storage systems and EV clusters with vehicle to grid（V2G）capabilities are integrated to establish an economic optimization scheduling model considering multisource collaboration. Then, aiming at the uncertain parameters in the model, the information gap decision theory is introduced, and a twolevel decision-making mechanism with both risk aversion and opportunity seeking is constructed. Finally, a virtual power plant is tested with an example to verify the correctness and effectiveness of the proposed model and algorithm. The results show that the method can realize the load side peak balancing and valley filling effectively, and has advantages in economy and stability.