Abstract: The access of large-scale electric vehicles (EVs) further exacerbates the load fluctuation of the distribution network, and demand response, as an important means of regulating the supply-demand balance, can effectively guide EV users to optimize their charging and discharging behaviors. However, the existing research fails to adequately consider the users’ willingness to respond and its variability, making it difficult to achieve efficient regulation. In this paper, we propose an optimal EV charging and discharging scheduling strategy that considers users' willingness to respond. Firstly, a quantitative model of battery loss anxiety cost and time anxiety cost is introduced to establish a non-economic response cost model for users. A dynamic charging and discharging incentive strategy is designed, which combines the affiliation function to dynamically classify user clusters and realize differentiated prediction of response willingness. Subsequently, in accordance with the Weber-Fechner law, the synergistic responsiveness surface of the charging price and electricity quantity is established to analyze the nonlinear effects of charging and discharging prices and state of charge (SOC) on users' response behaviors. Finally, a multi-objective optimization model is constructed to attain the dual optimization of distribution network operation costs and users' economic costs, and this model is subsequently solved using a particle swarm algorithm. The effectiveness of the strategy is validated through the improved IEEE 33-bus system, which balances the interests of both supply and demand and improves users’ response enthusiasm and grid economy.