Abstract: Considering travel characteristics of electric vehicles, this paper proposes a multi-time scale energy management strategy based on model predictive control for smart buildings with electric vehicles. Firstly, travel characteristics of electric vehicles are analyzed, and Monte Carlo is used to extract the arrival and departure time of electric vehicles, as well as the loading state at the beginning and end of charging. Secondly, the distributed power supply, energy storage and controllable load models of smart buildings are established, and the day-ahead energy management strategy is proposed based on mixed integer quadratic programming. Then, the intra-day rolling optimization strategy based on model predictive control is proposed to realize the dynamic correction of the day-ahead operation scheme. Finally, compared with a variety of energy management strategies, it is shown that the proposed strategy can effectively solve the problem of tie line power fluctuation caused by the day-ahead prediction error, improve the robustness of the system in the scenario of prediction uncertainty, reduce cost of the system, and improve the economy of the system.