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Task scheduling for heterogeneous multi-cloud environment is a well-known NP-complete problem. Due to exponential increase of client applications (i.e., workloads), cloud providers need to adopt an efficient task scheduling algorithm to handle workloads. Furthermore, the cloud provider may require to collaborate with other cloud providers to avoid fluctuation of demands. This workload sharing problem is referred as heterogeneous multi-cloud task scheduling problem. In this paper, we propose a task scheduling algorithm for heterogeneous multi-cloud environment. The algorithm is based on smoothing concept to organize the tasks. We perform rigorous experiments on synthetic and benchmark datasets and compare their results with two well-known multi-cloud algorithms namely, CMMS and CMAXMS. The comparison results show the superiority of the proposed algorithm in terms of two evaluation metrics, makespan and average cloud utilization.