Abstract: Optimization always plays a vital role to enhance the performance of the network. In this study, optimization is done with the recently proposed Relative Weight Based Clustering (RWC) algorithm to achieve minimum number of cluster heads in the network. The relative weight based clustering selects cluster heads based on the relative weights of the nodes. The optimal choosing of cluster heads enhances the performance of network by reducing the reaffiliation count and cluster head change. This study uses Genetic Algorithm (GA) to optimize relative weight based clustering based on the fitness function. The goal of genetic algorithm is to choose the one with highest fitness value as the best chromosome. Simulation study is carried out with the genetic algorithm optimization tool, LibGA and the improved performance metrics are shown for the optimized RWC and original RWC algorithm that enhances the stability of the network.