In the course of recent decades, cloud computing has turned into a hot research point for the logical, scholastic and mechanical networks. It is a wide-ranging term used to depict an extra class of framework-based enrolling that occurs over the web. The distributed computing principally plans to give capable access to remote and geologically disseminated assets. The other important purpose of cloud service providers is to gain maximum profit and use resources efficiently. As cloud technology is evolving day by day and confronts numerous challenges, one of them being uncovered is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a system. Every task needs a scheduling strategy which is assigned by the system in order to get executed by the processor. Procedures are vigorous to plan the trades for accomplishment. Job scheduling procedures supposed to be the most assumed difficulties in the cloud computing domain. The survey of existing papers reveals the better makespan time but cannot guarantee the proper balancing of load. To overcome this issue, Enhanced Multi-Objective Load balancing Scheduling Algorithm (EMOLB_LB) is proposed which uses Bee Colony Optimization algorithm for the analysis and balancing of load with more objective functions to sort the tasks and improvise the performance in terms of cost and time. The existing scheduling technique, Enhanced Multi-Objective Scheduling Algorithm (EMOSA) uses only non-dominating strategy for sorting the tasks but load management is not taken into consideration which is further optimized by proposing EMOLB_LB technique. The experimental results were analysed and compared with various existing techniques like Multi Objective Scheduling algorithm (MOSA), EMOSA and showed that the EMOLBA_LB technique was better than the earlier techniques in term of each performance attribute like average waiting time by 2.934%, processing cost by 17.6% and processing time by 20.5%.