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In the paper, real coded multi objective genetic algorithm based K-clustering method has been studied where K represents the number of clusters known apriori. The searching power of Genetic Algorithm (GA) is exploited to search for suitable clusters and cluster modes so that intra-cluster distance (Homogeneity, H) and inter-cluster distances (Separation, S) are simultaneously optimized. It is achieved by measuring H and S using Mod distance per feature metric, suitable for categorical features (attributes). We have selected 3 benchmark data sets from UCI Machine Learning Repository containing categorical features only. Here, K-modes is hybridized with GA to combine global searching capabilities of GA with local searching capabilities of K-modes. Considering context sensitivity, we have used a special crossover operator called “pairwise crossover” and “substitution”.