This article studied an optimization method for enterprise financial health assessment (FHA) model based on genetic algorithm (GA). In response to the problems of strong subjectivity, insufficient dynamism, and limited data processing capabilities in traditional evaluation models, this article optimized the weights of financial indicators through GAs to improve the scientific and accurate evaluation results. It used 11 financial indicators as the main data to collect financial data from multiple enterprises and form a dataset. GA can be used to optimize the weights of financial indicators. This article constructed a FHA model, evaluated the constructed model, conducted corresponding analysis based on experimental results, and drew conclusions. The results showed that the model optimized by GA had significant improvements in evaluation accuracy and adaptability. The maximum mean squared error (MSE) of the GA in all comparative experiments was 19.962, which was lower than the minimum MSE of 20.043 obtained by other comparative methods. The maximum Mean Absolute Error (MAE) of the GA in all comparisons was 4.988, which was lower than the minimum MAE of 5.031 obtained by other comparison methods. This indicates that the enterprise financial health evaluation model based on GA has sufficient accuracy, is more objective in determining the weights of financial indicators, and has higher scientific validity in the evaluation results. The optimized enterprise FHA model provides more effective services for enterprise financial management. By optimizing the enterprise FHA model based on GA, it provides scientific basis for enterprise decision-making, improves enterprise management level, and provides new ideas and methods for the application of GA in enterprise finance. Future research can further explore the combined application of GAs and other intelligent algorithms, explore more efficient optimization methods, and promote the development of FHA research for enterprises.