The uncertainty in many engineering problems can be handled through probabilistic, fuzzy, or interval methods. This paper aims to use a hybrid genetic algorithm for tackling such problems. The proposed hybrid algorithm integrates a simple local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective evolutionary algorithm. The work demonstrates the use of a technique alternating between optimization (general GA) and anti-optimization (local search). Local search utilizes specialized search engines that allow users to submit constrained searches. The algorithm has been tuned and its performance evaluated through specially formulated test problems referred to as 'Target Matching Problems' with multiple objectives. The results obtained indicate that the approach can produce good results at reasonable computational costs.