Music genre classification is a machine learning task that involves identifying the genre of a piece of music based on its audio features. The goal of this study can categorize music correctly into its genre using machine learning techniques. This is accomplished by gleaning useful features from the music and employing an effective classification model. Different datasets, input formats, features taken from datasets, machine learning approaches, and methods of deep learning used in previous research have all had varying degrees of success. In this work, we evaluate the performance of a deep neural network model by selecting effective music features using a genetic algorithm (GA) and optimizing hyper-parameters with Distributed Evolutionary Algorithm in Python (DEAP). The result shows that the proposed deep neural network by selecting features using GA and tuning hyper-parameter using DEAP provides better performance with an AUC of 1.00 and an accuracy of 98.61%.