Since the number of vehicles on the road has been dramatically increased, how to find out a 'good solution' to improve the traffic situation is a difficult optimization problem even if there is still plenty of room for improvement. The main focus of this study is on improving the quality of solutions for the traffic light optimization problem to minimize the waiting time of all the vehicles and maximize the number of vehicles arriving at the destination within a certain time period. An effective hybrid metaheuristic algorithm, called grey wolf grasshopper hybrid algorithm (GWGHA), that leverages the strength of grey wolf optimizer and grasshopper optimization algorithm will be presented in this paper to find a better result in solving the cycle traffic light problem (CTLP). The solutions obtained are simulated with a well-known microscopic traffic simulator named Simulation of Urban MObility (SUMO). Experiments were carried out based on data from Kaohsiung, Taiwan, Bahia Blanca, Argentina, and Ma laga, Spain that represent different experimental cases to evaluate the performance of GWGHA. The simulation results show that the proposed algorithm is superior to the state-of-the-art search algorithms compared in this paper for solving the CTLP.