This study proposes a method for wind-solar power generation forecasting based on Genetic Algorithm optimized BP neural networks. By integrating BP neural networks with Genetic Algorithms, the aim is to improve the accuracy of power generation forecasts. Initially, the BP neural network is trained using historical data comprising 60000 samples to create a preliminary prediction model. Subsequently, the Genetic Algorithm is used to optimize the weights and biases of the BP neural network, enhancing its predictive performance. Experimental results indicate that the optimized GA-BP neural network outperforms the traditional BP neural network in terms of prediction error, error rate, and prediction accuracy. This method offers an effective solution for wind-solar power forecasting and has the potential to significantly contribute to smart grid and renewable energy management.