Machine learning algorithms have been a hallmark of data mining in image and signal processing. Several studies have proposed various methods for improving classification accuracy. Artificial Neural network (ANN) is one of the most important data mining classification method among predictive algorithms. The performance of ANN is affected by several parameters such as a number of hidden layers neurons, learning function, stop conditions and network architecture. Parameter regulation is a point of critical challenge in this algorithm. The main purpose of this study is to provide a novel approach by using multi-objective genetic algorithm and ensemble classifier to obtain optimal parameters of ANN. To this end, first, a set of neural networks were trained by setting their parameters through the multi-objective genetic algorithm. Next, the best combination of neural networks was selected to make an ensemble classifier. This method was evaluated with five popular and available datasets. Three measurements; accuracy, time and ROC curve were considered to assess the efficiency. The experimental results show that the proposed approach can achieve a trade-off between time and accuracy by the multi-objective genetic algorithm. Moreover, using ensemble-classifiers approach, we increased the reliability of the model. Consequently, the proposed method promotes the detection accuracy in three of selected datasets in comparison of four recent suitable methods.