Many classical methods have been used in automatic sleep stage classification but few methods explore deep learning. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming. In this paper, we propose an efficient convolutional neural network, Sle-CNN, for five-sleep-stage classification. We attach each kernel in the first layers with a trainable coefficient to enhance the learning ability and flexibility of the kernel. Then, we make full use of the genetic algorithm’s heuristic search and the advantage of no need for the gradient to search for the sleep stage classification architecture. We verify the convergence of Sle-CNN and compare the performance of traditional convolutional neural networks before and after using the trainable coefficient. Meanwhile, we compare the performance between the Sle-CNN generated through genetic algorithm and the traditional convolutional neural networks. The experiments demonstrate that the convergence of Sle-CNN is faster than the normal convolutional neural networks and the Sle-CNN generated by genetic algorithm outperforms the traditional handcrafted counterparts too. Our research suggests that deep learning has a great potential on electroencephalogram signal processing, especially with the intensification of neural architecture search. Meanwhile, neural architecture search can exert greater power in practical engineering applications. We conduct the Sle-CNN with the Python library, Pytorch, and the code and models will be publicly available.