Abstract This paper presents a global optimization algorithm based on Subset Simulation for deterministic optimal design under general multiple constraints. The proposed algorithm is population-based realized with Markov Chain Monte Carlo and a simple evolutionary strategy. Problem-specific constraints are handled by a feasibility-based fitness function that reflects their degree of violation. Based on the constraint fitness function, a double-criterion sorting algorithm is used to guarantee that the feasible solutions are given higher priority over the infeasible ones before their objective function values are ranked. The efficiency and robustness of the proposed algorithm are illustrated using three benchmark optimization design problems. Comparison is made with other well-known stochastic optimization algorithms, such as genetic algorithm, particle swarm optimization and harmony search.