Aiming at the problems such as long task completion time， high task execution cost and unbalanced system load in task scheduling， a new cloud computing task scheduling method based on Orthogonal Adaptive Whale Optimization Algorithm （OAWOA） was proposed. Firstly， the Orthogonal Experimental Design （OED） was applied to the population initialization and global search stages to improve and maintain the population diversity， avoid the algorithm from falling into local convergence too early. Then， the adaptive exponential decline factor and bidirectional search mechanism were used to further strengthen the global search ability of the algorithm. Finally， the fitness function was optimized to enable the algorithm to achieve multi-objective optimization. Through the simulation experiments， the proposed algorithm was compared with Whale Optimization Algorithm （WOA）， Particle Swarm Optimization （PSO） algorithm， Bat Algorithm （BA） and two other improved WOAs. Experimental results show that， when the task scale is 50 and 500， the proposed algorithm achieves better convergence effect， has the total time and total cost of the obtained system executing tasks lower than those of other algorithms， and has the load balancing degree only lower than that of BA. In conclusion， the proposed algorithm shows significant advantages in reducing the total time and cost of system executing tasks and improving the system load balancing.