Utilizing dynamic resource allocation for load balancing is considered as an important optimization process of task scheduling in cloud computing. A poor scheduling policy may overload certain virtual machines while remaining virtual machines are idle. Accordingly, this paper proposes a hybrid load balancing algorithm with combination of Teaching-Learning-Based Optimization (TLBO) and Grey Wolves Optimization algorithms (GWO), which can well contribute in maximizing the throughput using well balanced load across virtual machines and overcome the problem of trap into local optimum. The hybrid algorithm is benchmarked on eleven test functions and a comparative study is conducted to verify the results with particle swarm optimization (PSO), Biogeography-based optimization (BBO), and GWO. To evaluate the performance of the proposed algorithm for load balancing, the hybrid algorithm is simulated and the experimental results are presented.