Abstract: With the increasing penetration of electric vehicles (EVs), reasonable charging and discharging scheduling has become crucial for maintaining the stable operation of power grids. To alleviate the stress on the grid caused by high EV integration and to enhance both grid economy and user participation, this study investigates the optimization of orderly charging and discharging scheduling based on vehicle-to-grid (V2G) technology. First, a charging and discharging modeling framework is established that accounts for EV travel behavior characteristics. A Monte Carlo simulation method is employed to model stochastic factors such as travel time periods, initial state of charge (SOC), charging demand, and electricity price fluctuations, thereby forming a random load profile. Subsequently, a multi-objective scheduling optimization model is developed, incorporating an improved Multi-Objective Frilled Lizard Optimization (MOFLO) algorithm. The objectives include grid load balancing, electricity operation cost, and EV user economic benefits. A Pareto optimal solution set is used to coordinate the interests of grid operators and vehicle owners. Experimental results demonstrate that the proposed method significantly enhances the performance of V2G scheduling. Compared with traditional strategies, it effectively reduces peak-valley differences in load, lowers operational costs, and increases economic returns for users, exhibiting strong adaptability and control capability. The proposed multi-objective optimization approach based on the improved MOFLO algorithm shows clear advantages in achieving vehicle-grid synergy, maintaining grid stability, and improving user participation benefits, thus providing valuable reference and technical support for the design of EV charging and discharging strategies in future smart grid (SG) environments.