This paper studies an evolutionary multiobjective optimization algorithm, called EVOLT, which heuristically optimizes QoS (quality of service) in communication networks for electric power utilities. EVOLT uses a population of individuals, each of which represents a set of QoS parameters, and evolves them via genetic operators such as crossover and mutation for satisfying given QoS requirements. Simulation results show that EVOLT outperforms a well-known existing evolutionary algorithm for multiobjective optimization and efficiently obtains quality QoS parameters with acceptable computational costs.