The environmental/economic dispatch (EED) problem is one of the topics that received much attention as a result of the increasing concern of global warming. This problem is essentially a constrained multiobjective continuous optimization problem, which aims to minimize the fuel cost and emission level simultaneously. In the literature, multiobjective evolutionary algorithms have shown promising results in solving this problem. Thus, we develop an algorithm based on adaptive differential evolution (DE). We investigate the performance of six popular DE mutation operators and three repair mechanisms using three public problem instances. Based on the experimental results, we decide to hybridize mutation operators and repair mechanisms in our algorithm. The proposed algorithm is able to reduce the fuel cost and emission level well in both single-objective and multiobjective manners.