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Sequential model-based optimization is a popular technique for global optimization of expensive black-box functions. It uses a regression model to approximate the objective function and iteratively proposes new interesting points. Deviating from the original formulation, it is often indispensable to apply parallelization to speed up the computation. This is usually achieved by evaluating as many points per iteration as there are workers available. However, if runtimes of the objective function are heterogeneous, resources might be wasted by idle workers. Our new knapsack-based scheduling approach aims at increasing the effectiveness of parallel optimization by efficient resource utilization. Derived from an extra regression model we use runtime predictions of point evaluations to efficiently map evaluations to workers and reduce idling. We compare our approach to five established parallelization strategies …