In this work we introduce a modified version of the simple genetic algorithm (MGA) and will show the results of its application to two GMA power law models (a general theoretical branched pathway system and a mathematical model of the amplification and responsiveness of the JAK2/STAT5 pathway representing an actual, experimentally studied system). The two case studies serve to illustrate the utility and potentialities of the MGA method for concerning parameter estimation in complex models of biological significance. The analysis of the results obtained from the application of the MGA algorithm allows an evaluation of the potentialities and shortcomings of the proposed algorithm when compared with other parameter estimation algorithm such as the simple genetic algorithm (SGA) and the simulated annealing (SA). MGA shows better performance in both studied cases than SGA and SA, either in the presence or absence of noise. It is suggested that these advantages are due to the fact that the objective function definition in the MGA could include the experimental error as a weight factor, thus minimizing the distance between the data and the predicted value. Actually, MGA is slightly slower that the SGA and the SA, but this limitation is compensated by its greater efficiency in finding objective values closer to the global optimum. Finally, MGA can lead to an early local optimum, but this shortcoming may be prevented by providing a great population diversity through the insertion of different selection processes.