<p>This study introduces an innovative simulation optimization framework to address network reliability optimization through component configuration. Unlike traditional approaches, this methodology uniquely integrates metaheuristic algorithms—simulated annealing (SA) and genetic algorithm (GA)—with Monte Carlo simulation (MCS), creating hybrid frameworks termed SA-MCS and GA-MCS. A key innovation lies in the framework’s ability to optimize network reliability by appropriately selecting the types and quantities of components within predefined constraints, achieving high efficiency without altering the network topology. Furthermore, the proposed approaches estimate network reliability without relying on the enumeration of lower-bound states or upper-bound states for demand <i>d</i>, significantly promoting computational efficiency. Numerical experiments on various network scenarios, including two real-world case studies of a wind power supply network and an academic computer network, demonstrate the effectiveness of these approaches. The results indicate that the proposed GA-MCS performs best in all experimental scenarios regarding computational efficiency. This work offers a novel tool for system administrators to enhance the robustness and performance of critical networks in industries such as power distribution and data transmission.</p>