Realistic high-dimensional MDO problems are more likely to have multimodal search spaces and they are also mutiobjective in nature. Genetic Alogrithms(GAs) are becoming popular choices for better global and multiobjective optimization frameworks to fully realize the full benefits of conducting MDO. One of the biggest drawbacks of GAs, however, is that they require many function evaluations to achieve a reasonable improvement within the design space. Therefore, the efficiency of GAs has to be

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... roved in some way before they can be truly used in high-fidelity MDO. In this work, a multiobjective design optimization framework is developed by combining GAs and an approximation technique called Kriging method which can produce fairly accurate global approximations to the actual design space to provide the function evaluations efficiently. It is applied to a low boom supersonic business jet design problem and its results demonstrate the efficiency and applicability of the proposed design framework. Furthermore, the possibility of using the Kriging approximation models as computationally inexpensive gradient estimators to accelerate the GA process is investigated.