In order to guide the demand-side electricity consumption behaviour of users, after investigating the whole social electricity consumption load of a county in southern China, according to the demand response, time-of-use electricity price and the reality of China's electricity market, this paper adopts the fast non-dominated sorting genetic algorithm with elite strategy to conduct a more in-depth optimization study on the demand-side management measures of three different users, and the conclusions are as follows: Combined with the practical problems in the electricity market transaction, a mathematical model with the minimum peak-valley difference of the load curve and the maximum power consumption comfort as the objective function is built, and the bundle of variables is established. The optimization results show that the multi-objective optimization on the demand side is slightly insufficient in smoothing the load curve, but considering the comfort of users' electricity consumption, the multi-objective optimization will improve its comprehensive performance and have stronger practicability, which can play a guiding role in the demand response behaviour of users and power trading in the power market, and provide a theoretical basis for actual trading.