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Design and optimization of intake and exhaust systems and valve timing is crucial in development of a naturally aspirated engine. Nowadays numerical simulation is a fundamental tool for this area. Unfortunately to perform an optimization of engine performance by setting even only a few parameters needs great effort in terms of time and engineering resources even with simple architecture engines. To overcome this problem the authors have developed an optimization methodology: the use of a 1_D simulation code allows one to build a neural network (NN) that characterizes engine working conditions for several input data variations (such as intake/exhaust systems and valve timing). A genetic algorithm (GA) coupled with the neural network is used to carry-out the multi-parameter optimization of engine performance.