Abstarct: Wireless sensor networks are used to receive information such as temperature, humidity, rainfall, etc. from the environment. These networks face challenges such as data processing, communications, and sensor management. Challenges need to be given more consideration when these networks are used in uncertain, dynamic environments with bandwidth and energy constraints. Since energy resources, bandwidth, and processing power are constantly changing in these networks, the necessary management must be done on the network structure and energy resources to keep the network activity for a longer period of time. In this thesis, the use of evolutionary algorithms in clustering and routing is proposed to reduce energy consumption in wireless sensor networks. For this purpose, a particle swarm algorithm has been used in the routing and clustering of wireless sensor networks. To cluster and determine the eclipse according to the network structure, a suitable objective function is presented. The data in each cluster is collected by the header and then a suitable path for data exchange between the headers and the center is determined. The results and comparisons with the work of others are shown graphically. The simulation results and its comparison with the Leach method show a 15% improvement in energy consumption, so with this method, network life is also improved.