Cloud computing is necessary for the current global computing demand since it is becoming difficult to maintain disk space and power requirements for individual users. The dependency on computing is increasing day by day, thus increasing the pressure on the infrastructure required to meet those needs. Cloud computing is the solution to cater to this problem. When the load on a cloud is increased, it becomes imperative to manage the load or distribute the load judiciously to make job scheduling on the cloud as fair as possible. No server should ideally either be underloaded or overloaded. This equilibrium is to be maintained for the smooth functioning of the cloud environment. This research work aims to develop an energy efficient algorithm meant for the cloud setup that distributes the load evenly and has a substantial enhancing impact on the throughput of the entire system as well. These two subgoals when achieved, will permit the load distribution to move towards the main goal of achieving higher energy efficiency also and translating the entire system towards a greener system. To realize this, this paper proposes a hybrid optimization algorithm with a unification of biogeography-based optimization algorithm and genetic algorithm. The results of the new algorithm are presented in the paper and were found to lead to an increase in throughput and a reduction in scheduling cost while the number of tasks submitted is increased.