A surrogate-assisted evolutionary optimization method, ESP, discovers decision strategies in real-world applications. Based on historical data, a surrogate is learned and used to evaluate candidate policies with minimal exploration cost. Extended into sequential decision making, ESP is highly sample efficient, has low variance, and low regret, making the policies reliable and safe. As an unexpected result, the surrogate also regularizes decision making, making it sometimes possible to discover good policies even when direct evolution fails.