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Multi-cluster environments are composed of multiple clusters that act collaboratively, thus allowing computational problems that require more resources than those available in a single cluster to be treated. However, the degree of complexity of the scheduling process is greatly increased by the resources heterogeneity and the co-allocation process, which distributes the tasks of parallel jobs across cluster boundaries. in this paper, we use the Bulk-Synchronous Parallel model on which the jobs are composed of a fixed number of tasks that act in a collaborative manner. We propose a new MIP model which determines the best allocation for all the jobs in the queue, identifying their execution order and minimizing the overall make span. the results show that the proposed technique produces a highly compact scheduling of the jobs, achieving better resource utilization and reducing the overall make span. This makes the OAS technique especially useful for environments dealing with limited resources and large applications.