In this paper we present an intelligent approach for Computer Aided Design, that is capable to learn from its experience in order to speedup the design process. The proposed approach integrates two well known soft-computing techniques, Multi-Objective Genetic Algorithms (MOGAs) and Fuzzy Systems (FSs): MOGA smartly explores the design space, in the meanwhile the FS learn from the experience accumulated during the MOGA evolution, storing knowledge in fuzzy rules. The joined rules build the Knowledge Base through which the integrated system quickly predict the results of complex simulations thus avoiding their long execution times. The methodology is applied to a real case study and evaluated in terms of both efficiency and accuracy, demonstrating the superiority of the intelligent approach against brute force random search.