The optimal power flow (OPF) problem is a large-scale, non-linear and non-convex optimization task that underpins the economic and secure operation of modern power systems. When several conflicting goals—fuel cost, active power loss and emission—are pursued simultaneously, no single operating point optimizes all objectives, and a set of trade-off solutions must be produced. This paper applies an archive-based multi-objective Ions Motion Optimization algorithm (MOIMO) to the multi-objective OPF (MOOPF) problem. The single-objective Ions Motion Optimization is extended to many objectives through an external archive that retains the non-dominated solutions found so far and a leader-selection strategy that steers the search toward the least-crowded region of the Pareto front, thereby preserving diversity. MOIMO is benchmarked against six contemporary archive-based optimizers on ten MOOPF cases formulated over the IEEE 30-, 57- and 118-bus systems, with realistic cost models that include valve-point loading, multi-fuel and prohibited operating zones. Performance is judged by the best compromised solution, Pareto-front spread, computation time, hyper-volume and three statistical tests. MOIMO returns uniformly distributed Pareto fronts and the lowest standard deviation of hyper-volume in four of ten cases, confirming its robustness, while statistical tests rank MOMVO first overall, in line with the No Free Lunch theorem. A techno-economic-environmental assessment of the quadratic-fuel-cost-and-emission compromise solution shows an annual fuel saving of 117,988 $ and an emission reduction of 298.716 tons relative to a reported method, demonstrating the practical value of the approach.