In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems. To evaluate the efficiency of the proposed algorithm, we examined it on the traveling salesman problem (TSP) using some benchmark from TSPLIB online library of instances for the TSP. The experimental results indicate that the monarchy metaheuristic is competitive with the other methods that exist in the literature for finding approximate solutions.