Deep learning has improved the state-of-the-art in sentiment analysis for various languages, including Arabic. One aspect that can affect the performance of deep learning-based sentiment classification is the optimization method used for training the neural network. The conventional optimization method is carried out by a backpropagation (BP) algorithm that relies on gradient descent to find the minimum of a cost function. However, BP has the tendency to converge into local minima instead of global minima since neural networks generate complex error surfaces for even simple problems. In this study, for the purpose of improving the Arabic sentiment classification, we propose to use a genetic algorithm (GA) to train a deep neural network (DNN). GA is a meta-heuristic optimization algorithm inspired by the theory of natural evolution. The algorithm is expected to improve the classifier's performance due to its capability to reach optimal or near-optimal solutions. The proposed method uses Arabic sentiment lexicons to extract various features considering different aspects for text representation. The effectiveness of the proposed method is evaluated by analyzing its performance, versus a DNN trained with BP algorithm. The experimental results show that the proposed method can present better F1-measure of 90.7% for Arabic sentiment classification than traditional BP-based DNN.