This study presents an enhanced evaluation model for enterprise environmental behavior (EEB) based on a GA-optimized neural network framework. By integrating environmental credit theory with neural network learning capabilities, the proposed model addresses limitations of traditional credit scoring systems, such as slow convergence and static evaluation. A credit rating system with four grades is established using 28 input indicators—six qualitative and 22 quantitative. The model’s architecture and training parameters are carefully optimized using a genetic algorithm, which accelerates convergence and enhances prediction performance. Comparative experiments demonstrate that the GA-improved model reduces training steps by 25.47% and increases average evaluation accuracy from 73.68% to 80.11%. These results underscore the model’s potential as an intelligent and robust tool for EEB credit assessment.