This paper describes a technique for design under uncertainty based on hybrid genetic algorithm. In this work, the proposed hybrid algorithm integrates a simple local search strategy with a constrained multi-objective evolutionary algorithm. The local search is integrated as the worst-case-scenario technique of anti-optimization. When anti-optimization is integrated with structural optimization, a nested optimization problem is created, which can be very expensive to solve. The paper demonstrates the use of a technique alternating between optimization (general genetic algorithm) and anti-optimization (local search) which alleviates the computational burden. The method is applied to the optimization of a simply supported structure, to the optimization of a simple problem with conflicting objective functions. The results obtained indicate that the approach can produce good results at reasonable computational costs.