Metaheuristic algorithms are powerful tools for solving optimization problems. The Krill Herd Algorithm (KHA) is a new nature-inspired metaheuristic algorithm. A Multi-Agent System (MAS) is a system that contains multiple interacting agents. These agents are autonomous entities that interact with their environment to achieve specific goals. Agents can also learn or use their knowledge to accomplish a task. Multi-agent systems can solve problems that are very difficult or even impossible for monolithic systems to solve. In this paper, a modification of KHA is proposed which incorporates MAS to obtain a Multi-Agent Krill Herd Algorithm (MA-KHA). The performance of the proposed algorithm is evaluated using several benchmark global optimization problems. Numerical results are presented which show that MA-KHA performs better than existing krill herd algorithms.