This paper deals with a specific class of location problems, in which given number of service centers is ti be located to optimize certain goal. Changes in the service center deployment following from the results of the associated decision-making process are often expected to be applied for a long period and thus, the obtained result has a strategic importance. When there are two or more different and contradictory goals in the location problem, the Pareto front of solutions indicates the one and only optimal answer. There may be an infinite number of solutions to a problem, but finding the exact Pareto front is still no easy feat. Thus, in contrast to the precise computationally time-intensive methods, various metaheuristics can quickly give a user a good approximation of the Pareto front. When a solution deviates from the Pareto front, the permissible computational time of metaheuristics compensates for it. This work presents a metaheuristic that falls on a spectrum of approaches to finding non-dominated solutions to the public service system design problem where two competing criteria are present. Using the ant colony optimization principle, the metaheuristic uses the current set of non-dominated solutions to minimize the difference from the Pareto front. To assess the performance of the suggested metaheuristic, a set of numerical trials is conducted using known benchmarks whose precise Pareto fronts are utilized.