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We address in this paper the task-scheduling in cloud computing. This problem is known to be -hard due to its combinatorial aspect. The main role of our model is to estimate the time needed to run a set of tasks in cloud and in turn reduces the processing cost. We propose a genetic approach for modelling and optimizing a task-scheduling problem in cloud computing. The experimental results demonstrate that our solution successfully competes with previous task-scheduling algorithms. For this, we develop a decision support system based on the core of CloudSim. In terms of processing cost, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of makespan, the obtained schedules are also shorter than those of other algorithms.