It is well known that anode and cathode pressures, cell temperature and channel geometry are the effective parameters in the performance of DMFC (direct methanol fuel cell). In the present paper, the GA (genetic algorithm) as one of the most powerful optimization tools is applied to determine the optimal values for these parameters which result in maximum power density of a DMFC. The predominant part of the genetic algorithm is the fitness function. For the fitness function calculation, calculation of more than one thousand cases is necessary. Unfortunately, large numbers of experiments are needed, which is very time-consuming and costly. To overcome this challenge, a quasi two dimensional, isothermal model is used to obtain the power of DMFC as the fitness function of GA. For validation of this model, the results of the model are compared with experimental results and literature and shown to be in good agreement with them. (C) 2014 Elsevier Ltd. All rights reserved.