In this paper we propose an original algorithm for mixed data and task parallel scheduling. The main specificities of this algorithm are to simultaneously perform the allocation and scheduling processes, and avoid data replication. The idea is to base the scheduling on an accurate evaluation of each task of the application depending on the processor grid. Then no assumption is made with regard to the homogeneity of the execution platform. The complexity of our algorithm is given. Performance achieved by our schedules both in homogeneous and heterogeneous worlds, are compared to data-parallel executions for two applications: the complex matrix multiplication and the Strassen decomposition.