The task scheduling of Genetic Algorithm (GA) in cloud environment has problems such as poor optimization ability and unstable results. For the above problems, a Variance-Directional Variation GA (V-DVGA) was proposed. In the selection part, multiple selections were made in the process of each iteration, and the mathematical variance was used to ensure the diversity of the population and expand the search range of the better solution. In the intersection part, a new intersection mechanism was established to enrich the diversity of the population and improve the overall fitness of the population. In the variation part, the variation method was improved, the directional variation was used on the basis of the traditional variation to increase the optimization ability of the algorithm. The cloud environment simulation experiments were carried out on the workflowSim platform, and the proposed algorithm was compared with the classical GA and the current Workflow Scheduling Algorithm based on Genetic Algorithm (CWTS-GA). The experimental results show that under the same setting conditions, the proposed algorithm is superior to the other two algorithms in terms of execution efficiency, optimization ability and stability, and is an effective task scheduling algorithm in cloud computing environment.