Artificial neural networks (ANNs) have been widely applied in data mining as a supervised classification technique. The accuracy of this model is mainly provided by its high tolerance to noisy data as well as its ability to classify patterns on which they have not been trained. Moreover, the performance to ANN based models mainly depends both on the ANN parameters and on the quality of input variables. Whereas, an exhaustive search on either appropriate parameters or predictive inputs is very computationally expansive. In this paper, we propose a new hybrid model based on genetic algorithms and artificial neural networks. Our evolutionary classifier is capable of selecting the best set of predictive variables, then, searching for the best neural network classifier and improving classification and generalization accuracies. The designated model was applied to the problem of bankruptcy forecasting, experiments have shown very promising results for the bankruptcy prediction in terms of predictive accuracy and adaptability.