Abstract: In order to meet the differentiated business requirements of power distribution scenarios, comprehensively considering the coverage and connectivity of the network, we construct a wireless and power line communication (PLC) integrated communication coverage model, propose an improved multi-objective sparrow coverage optimization algorithm based on a hybrid strategy. First, we establish wireless and PLC integrated communication multi-objective coverage optimization problem. Secondly, we use the method of combining tent mapping and lens imaging reverse learning is used to initialize the population of the Sparrow algorithm to increase the diversity of the population. At the same time, we propose a two-stage adaptive convergence factor to adjust the ratio of explorers and followers during the operation of the Sparrow algorithm. We also improve the existing boundary processing strategy and introduce the crossover mutation strategy to improve the global search capability and search accuracy of the algorithm. Finally, we introduce fast non-dominated sorting, crowding strategy and optimal external archiving, and change the single-objective Sparrow algorithm into a multi-objective algorithm. The simulation results show that compared with the multi-objective genetic algorithm, multi-objective particle swarm algorithm, and multi-objective evolutionary algorithm, the proposed improved multi-objective Sparrow algorithm increased the target area coverage by 5.2%, 3.17% and 5.93% respectively. In addition, the Pareto optimal solution set obtained by the proposed algorithm provides a variety of solutions for the deployment of wireless and PLC integrated communication network nodes, and it has important guiding significance for the planning and construction of power distribution communication networks.