The multi-objective dynamic economic emission dispatch (DEED) problem in electric system is a complex optimization problem. A hybrid method using the oppositional-based learning sine cosine algorithm is introduced to get solution of problem, aiming to minimize both the entire cost of fuel and total emissions produced by the power system. Ten heat-generating units are used in four separate test systems for the experiments. The DEED problem without electric vehicles (EVs) is represented in the first test system, while the second test system considers load leveling as a way to reduce the peak load. In the third test system, EVs are considered as energy sources, and in the fourth test system, EVs are incorporated into the DEED problem. The efficiency of the suggested approach is contrasted with that of recently created algorithms. The outcomes show that the suggested strategy performs better than the other algorithms. The integration of EVs into the DEED issue has a sizable influence on the overall cost of fuel and total emissions of the power system, according to the optimization findings attained using the suggested technique. In conclusion, the DEED problem in the electric power system can be effectively solved by the proposed hybrid method using the oppositional-based learning sine cosine algorithm. This research work provides useful insights into the complex optimization problem in the electric power system and paves the way for future research in this area.