A Wireless Sensor Network (WSN) is a collection of hundreds or thousands of sensor nodes that are wirelessly linked together to monitor, manage, and track a wide range of applications, including those in the fields of medicine and health care, environmental monitoring, home automation, industrial and military applications, etc. one of the most and the major challenges of WSNs is energy efficiency problems due to the small and limited battery power of the sensor nodes. The purpose of this research study is to reduce the overall energy consumption and maximize the lifetime of the network. One of the key ways to solve this problem needs proper design of clustering and routing techniques. We used a hybrid method named Genetic algorithm and Cat Salp Swarm optimization Algorithms (GAC-SSA), which is a combination of the Genetic Algorithm (GA) and the Cat-Salp Swarm (C-SSA) algorithm. We employ GA to select the best cluster head (CH) from a collection of nodes based on five factors, namely, residual energy, the distance between CH and a node, the distance between CH and BS, node centrality, and node degree. Using the C-SSA algorithm, which was created by combining the salp swarm optimization (SSA) and cat swarm optimization (CSO) algorithms, the multi-hop route between the CH and from the CH to the Base Station (BS) is determined. Based on energy, distance, intra-cluster distance, and distance between clusters, it chooses the optimum route. The proposed methodology is implemented through simulation using MATLAB software and performance validation of the GAC-SSA is done against the most popular cluster-based hierarchical techniques of LEACH, GECR, and other two GA and C-SSA-based existing techniques. The simulation results show that the proposed methodology GAC-SSA have better energy consumption and network lifetime than the existing algorithms. Keywords: Cat-salp swarm algorithm, cluster head, Energy consumption, Genetic algorithm; Multi-hop routing, Wireless sensor network.