Software reliability models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to predict the software reliability growth models (SRGM) however; none of them has proven to perform well considering different project characteristics. The variability of predictive accuracy seems mainly due to the unrealistic assumptions in each model, there is no single model yet available has been shown to be sufficiently trustworthy in most or all applications. Genetic Algorithms can proposed the solution by overcome the uncertainties in the modeling. This is dependent on the successful software runs by combining multiple models using multiple objective function to achieve the best generalization performance where. The objectives are conflicting and no design exists which can be considered best with respect to all objectives. In this paper, experiments were conducted to confirm these hypotheses. Then evaluating the predictive capability of the ensemble of models optimized using multi-objective GA has been calculated. Finally, the results were compared with traditional models.