There is a lot of uncertainty in the response of electric vehicle (EV) users to demand-side regulation. When the charging information of EVs is incomplete, there are still comparatively few methods to efficiently deal with the uncertainty of charging behavior. Therefore, this paper introduces information gap decision theory (IGDT) to accurately model the user response uncertainty, and quantitatively evaluate the corresponding opportunity benefits and risk losses. First, in order to quantitatively analyze the acceptable deviation between the aggregator's predicted response rate and the actual response rate of EVs, the determinism cost decision problem of the aggregator is transformed into an optimization problem that takes into account the uncertainty of the EV's response. Second, according to the aggregator's acceptance of the risk cost of the strategy, the scheduling model is divided into optimistic and pessimistic types. Based on the IGDT, the corresponding opportunity and robust strategies are respectively generated according to the deviation between the given and the expected cost. Aggregators of different risk acceptance can adopt corresponding optimization strategies based on the results of the proposed algorithm to ensure expected returns. Finally, the simulation results show that in a limited charging information environment, the proposed algorithm can effectively deal with the uncertainty of EVs’ response and reduce the total dispatch cost of EV charging aggregators. This work is supported by the National Natural Science Foundation of China (No. 51977127).