This paper presents a multi-objective differential-evolution-based approach to solve the optimal power flow (OPF) problem. The OPF problem has been treated as a true multi-objective constrained optimization problem. Different objective functions and operational constraints have been considered in the problem formulation. A clustering algorithm is applied to manage the size of the Pareto set. In addition, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Simulation results on IEEE 30-bus and IEEE 118-bus standard test systems show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well-distributed Pareto-optimal solutions.