The disorderly access of large-scale electric vehicles (EVs) will have adverse effects on the microgrid, such as increasing the peak-valley difference, decreasing the power quality, and increasing the difficulty in microgrid operation optimization and control. To this end, the author proposed an EV to microgrid (V2M) interaction scheduling strategy using cluster optimization to balance power demand and supply in the microgrid. First, in the lower-level vehicle-to-aggregator (V2A) stage, the EVs in each period are dynamically divided into regular and regulated clusters according to their battery, time, and charging/discharging conversion time constraints, with the regular cluster carrying out disorderly charging and the regulated cluster containing charging and discharging clusters. Then, in the upper-level aggregator-to-microgrid (A2M) stage, the dispatchable load of the control cluster is optimized at the control center to minimize the total load variance during the study period, using the cluster division and cluster load information as constraints. Finally, the power allocation algorithm is used to realize the spatial and temporal distributions of the EV cluster charging demand and discharging capacity for the scheduling strategy of V2A and A2M interactions. The proposed method can ensure that the EVs can cut the peak and fill the valley of the microgrid while meeting the travel demand.