The disorderly charging of large-scale electric vehicles connected to the power grid will lead to excessive load variance in the distribution network system. Fully utilizing the dual characteristics of electric vehicles can reduce the load variance of the power grid and achieve efficient utilization of green electricity, but user scheduling capacity is an important factor affecting the application of vehicle-to-grid interaction. This article applies the Monte Carlo method and the improved multi-objective particle swarm optimization algorithm with niche technology (niche-MOPSO) to study the multi-objective optimization strategy of grid-connected photovoltaics and V2G operation based on the impact of scheduling capacity. The research results indicate that as the charging participation rate of EVs gradually increases, disordered EV charging loads will lead to an increase in grid side load variance, but the impact on users' charging costs is relatively small. With the increase in EV scheduling capacity in the work area, the photovoltaic consumption rate gradually decreases, and the load variance shows a trend of first decreasing and then increasing. When the scheduling capacity is 30%, the load variance reaches its minimum, indicating that reasonable V2G calling is beneficial to the stability of power grid operation. Under the same scheduling capacity, the niche-MOPSO algorithm reduces the load variance and peak load, and also lowers user charging costs or increases user revenues. Moreover, the revenue under the V2G price incentive mechanism is much greater than that under the time-of-use electricity price mechanism. The niche-MOPSO algorithm can effectively optimize both load variance and user charging cost.