ELECTRE TRI is the most applicable and developed outranking based classification method in the field of MCDA. By including a large number of parameters, it provides a huge amount of information on criteria which enriches decision making process, although calculation of these large number of parameters is very time consuming and difficult task. To tackle this problem, this paper proposes a new method called NSGA-ELECTRE, by which the NSGA- algorithm learns ELECTRE TRI and elicits its parameters through an evolutionary process. The proposed method contributes to the literature by utilizing a pair of conflicting objective functions including Type I errors and Type II errors instead of using a single criterion named “classification accuracy” which used frequently in the related works. The proposed bi-objective method is applied to six known credit risk datasets. The NRGA model is used as a benchmark for validation. Computational results indicate outstanding performance of the NSGA-ELECTRE method.