Convolutional neural networks (CNNs) exhibit superior performance in image feature extraction, making them extensively used in the area of traffic sign recognition. However, the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks, necessitating image preprocessing and model parameter tuning. This increases the complexity of the model design process. This study introduces an evolutionary neural architecture search (ENAS) algorithm for the automatic design of neural network models tailored for traffic sign recognition. By integrating the construction parameters of residual network (ResNet) into evolutionary algorithms (EAs), we automatically generate lightweight networks for traffic sign recognition, utilizing blocks as the fundamental building units. Experimental evaluations on the German traffic sign recognition benchmark (GTSRB) dataset reveal that the algorithm attains a recognition accuracy of 99.32%, with a mere 2.8×106 parameters. Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures, significantly reducing the number of network parameters while maintaining recognition accuracy.