Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multiobjective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which consider several conflicting objectives with varying tradeoff levels. This article proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the tradeoff levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a self-adaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the above features into a fast and efficient algorithm. The proposed algorithm has been validated using the IEEE 30-bus system and a revised 33-bus radial system added to one node of the 30-bus system. The simulation results show that the proposed algorithm can provide a good accuracy and can converge to a set of optimal solutions of the MOPFs.