This paper presents the optimization of expanding velocity for tube expanding process in the manufacturing of a heat exchanger. In specific, the expanding velocity has a great influence on the performance of a heat exchanger because it is a key variable determining the quantity of tube expending at assembly stage as well as a key Parameter determining overall production rate. The simulation showed that the genetic algorithm used in this paper resulted in the optimal tube expanding velocity by performing the following series of iteration; the generation of arbitrary population for tube expanding parameters, consequently the generation of tube expanding velocities, the evaluation of tube expanding quantity using the pre-trained data of plastic deformation by means of a neural network and finally the generation of next population using a penalty faction and a Roulette wheel method.