Innovative aircraft design studies have noted that uncertainty effects could become significant and greatly emphasized during the conceptual design phases due to the scarcity of information about the new aero-structure being designed. The introduction of these effects in design methodologies are strongly recommended in order to perform a consistent evaluation of structural integrity . The benefit to run a Robust Optimization is the opportunity to take into account uncertainties inside the optimization process obtaining a set of robust solutions. A major drawback of performing Robust Multi-Objective Optimization is the computational time required. The proposed research focus on the reduction of the computational time using mathematic and computational techniques. In the paper, a generalized approach to operate a Robust Multi-Objective Optimization (RMOO) for Aerospace structure using MSC software Patran/Nastran to evaluate the Objectives Function, is proposed. A Multi-Objective Differential Evolution Algorithm with a K-NN surrogate model and named MODE-LD+SS-KNN, is used. The robust evaluation is obtained via a Quasi Montecarlo Method using Sobol sequence (QMM), the uncertainties due to material and manufacturing process are modeled via Composite Micromechanics Theory. Example of applications presented include the optimization process for a composite flat plate for minimum weight and maximum uniaxial buckling load. The proposed approach is compared with classical Robust Multi-Objective Optimization method in terms of computational time and a reduction up to one order of magnitude has been pointed out. The computational time reduction makes the Robust Optimization a more suitable choice in comparison with non-Robust Optimization when uncertainty should be included in the optimization loop.