Feature selection is a process for the elimination of irrelevant and redundant features from a dataset in order to improve learning performance in terms of accuracy and time to build a model from the subsets. The conventional techniques in this regard have limitations such as the high computational overhead for training, even in moderate datasets. Although attention has been paid to the development of rapid and accurate detection techniques, finding a dataset of features that could increase detection accuracy is paramount. The issue with feature selection is the NP-hard problem; therefore, an optimal solution cannot be guaranteed. The present study aimed to propose a new solution for the non-dominated sorting genetic algorithm (NSGA II) by making it binary through the Sigmoid transfer function and a thresholding device for binary feature selection in order to improve the performance in feature selection problems in terms of the accuracy and reduction of the subset dimensions. In addition, the efficiency of the proposed algorithm in reducing the mentioned parameters was measured through comparison with other methods in the four datasets of breast cancer, hepatitis, heart, and diabetes.