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This paper gives a short review of real coded genetic algorithm (RCGA) used for multiobjective optimization. Handling of continues search space is very easy with RCGA and solution representation is very close to natural formulation of real-world problems. Because of the obvious reasons, most of real-world multi-objective optimization problems are solved using RCGA. The topics discussed in this paper include new algorithms, design issues of multi-objective optimization like efficiency, scalability, constraint handling and self-adaptation. This discussion suggests potential areas for future research, namely, design of new algorithm, new recombination operator and Pareto optimal front formation techniques.