In this paper, the authors present a review on genetic algorithms based on clustering methods. Clustering is an important form of data mining. It can be used to extract useful and hidden information from the datasets. Clustering techniques have a large area of applications including bioinformatics, web use data analysis and image analysis etc. Traditional clustering algorithms applied to datasets most of the times result in sub-optimal solution due to large search space, so evolutionary algorithms particularly genetic algorithms are best suited for the clustering tasks. The capability of genetic algorithms is applied to find optimally disjoint partitions and proper number of clusters for a dataset.