Number of nature inspired algorithms is proposed to solve complex optimization problems. The Krill Herd algorithm is one such biologically-inspired algorithm, proposed to solve optimization problems in response to biological and environmental processes. It is mainly based on the simulation technique of the herding behavior of the krill swarms. The objective function is defined as the combination of minimum distance of the krill individual from food and from the highest density of the swarm. The position of the krill individual is mainly influenced by three important factors, (i) movement induced by other krill individuals, (ii) foraging activity, and (iii) random diffusion. The process is mimicked to find optimum solution of the algorithm. In this paper, I implemented and evaluated the algorithm using five different benchmark functions, namely Alpine, Ackley, Griewank, Rastrigin and Sphere. The results obtained are satisfactory and proves the Krill herd algorithm’s efficiency in solving the optimization problems.