In this talk, we introduce a novel approach leveraging genetic algorithms to optimize both forecast stability and accuracy, creating a dynamically weighted ensemble that balances these competing objectives and delivering better accuracy than any single base model. By incorporating past model performance into our evolutionary framework, we iteratively evolve an ensemble that minimizes large forecast swings while maintaining or improving overall accuracy. We demonstrate how this method systematically adjusts model weights based on historical deviations and performance metrics, solving a key business challenge.