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Cellular Manufacturing (CM) is an approach to harness the benefits of high production rate of a flow shop while maintaining flexibility, and utilizing facilities, of a job-shop. CM necessitates that parts and machines are allocated into cells to produce the identified part families so that productivity and flexibility of the system can be improved. In this paper, an attempt has been made to propose a clustering methodology based on Non-dominated Sorting Genetic Algorithm II (NSGA II) in which multiple objectives ie inter cellular movements and within cell load variations are considered to generate the pareto front. The processing data like operation sequence, machine capacity, processing time and batch sizes have been considered to form the realistic generalized cells. The results support the better performance of the proposed algorithm. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.