In this paper we present a heuristic algorithm for solving the parallel query plan scheduling problem in data grids. The algorithm exploits the binary tree structure of the query plan to determine profitable allocations. It takes care of multiple forms of parallelism while allocating resources to the subplans. Previous approaches towards solving the problem either ignores partitioned parallelism by focusing solely on pipelined parallelism wherein the communication cost is minimized or lays more emphasis on partitioned parallelism at the cost of pipelined parallelism. The work in this paper attempts to take care of both pipelining and partitioning mechanisms while optimizing the allocation of resources to the query sub-plans. The algorithm is particularly well suited for allocation of resources to sub-plans in a bushy query plan - which is the most profitable form of query plan in distributed database query optimization.