Congestion management is vital for power system operation, ensuring efficient and reliable electricity transmission. Traditional methods, such as load shedding, can result in economic losses and dissatisfaction. This paper suggests an innovative congestion management approach using generation rescheduling optimized by the enhanced coati optimization algorithm (ECOA). Inspired by coatis’ hunting behavior, ECOA efficiently explores the search space, converging toward optimal solutions. In congestion management, ECOA optimizes power plant schedules to ease transmission line congestion, minimizing system operation costs. The proposed method is tested on the IEEE-30 Bus System, a standard power system analysis case. Results show that ECOA-based rescheduling effectively reduces congestion without compromising security or increasing costs. Additionally, it outperforms Genetic Algorithm and Particle Swarm Optimization in congestion alleviation and solution quality.