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Mobile Ad hoc Networks (MANET) are wireless infrastructure less networks that are formed spontaneously and are highly dynamic in nature. Clustering is done in MANETs to address issues related to scalability, heterogeneity and to reduce network overhead. In clustering the entire network is divided into clusters or groups with one Cluster Head (CH) per cluster. The process of CH selection and route optimization is extremely crucial in clustering. Genetic Algorithm (GA) can be implemented to optimize the process of clustering in MANETs. GA is the most recently used advanced bio-inspired optimization technique which implements techniques of genetics like selection, crossover, mutation etc. to find out an improved solution to a problem similar to the next generation that inherits the positive traits and features of the previous one. In this chapter several genetic algorithm based optimization techniques for clustering has been discussed. A comparative analysis of the different approaches has also been presented. This chapter concludes with future research directions in this domain.