This article describes a multiobjective genetic fuzzy clustering scheme that utilizes the search capabilities of NSGA-II, a popular multiobjective genetic algorithm and optimizes a number of fuzzy cluster validity measures. Real-coded encoding of the cluster centers is used for this purpose. The multiobjective clustering scheme produces a number of non-dominated solutions, each of which contains some information about the clustering structure. Hence it is required to obtain the final optimal clustering by combining those information. For this, clustering ensemble is used to combine the non-dominated solutions of the final Pareto front produced The proposed method is applied on several simulated TI-weighted, T2-weighted and proton density-weighted normal MRI brain images. Superiority of the proposed method over K-means, Fuzzy C-means, Expectation Maximization and Single Objective Genetic clustering have been demonstrated.