Aiming at the problem of load dispatching under demand response, it is necessary to provide a response scheme to meet the interests of residents and improve the stability of power grid operation. This paper considered electricity price, incentive demand response mechanism and residential electricity demand, and established a multi-user load scheduling high-dimensional objective optimization model with the goal of minimizing electricity cost and community load variance. Combining with the characteristics of the model, this paper proposed a cooperative co-evolutionary differential evolution algorithm based on multi-strategy. It designed a hybrid coding and population initialization strategy based on the characteristics of residential electricity consumption to improve the quality of the solution. It introduced the idea of cooperative co-evolution to decompose the problem variables, and divided the population according to the high-dimensional target grouping and aggregation to avoid falling into local optimum. In the evolution of each sub-population, it adopted a double differential mode coordination strategy, and constructed knowledge transfer individuals to realize information interaction between populations. Finally, it retained the complete excellent solution to the external file through the population merging strategy combining greedy and random selection to improve the convergence and distribution of the Pareto optimal set. Simulation results show that the proposed method can reduce the electricity cost of community residents by about 18% and the variance of load fluctuation by more than 30%. As the number of residents increases, the convergence and diversity of the algorithm are more obvious than other algorithms in the same field.