IMRT uses radiation beams with different angles and intensities to target cancerous tissues while protecting healthy organs. Planning methods based on the generalized Equivalent Uniform Dose metric produce plans with excellent tumor coverage, but necessitate the adjustment of many parameters. To address this challenge, a novel approach, PersEUD, has been proposed for the automated tuning of these parameters. This is achieved by combining solutions from a Gradient Descent algorithm with an evolutionary optimization method to explore the parameter space efficiently. Previous research has demonstrated the effectiveness of this approach in meeting clinical constraints. However, its high computational demands hinder its integration into clinical practice. The goal of this study is to accelerate the optimization processes by distributing the evaluations in the nodes of modern multicore clusters. At the node level, these evaluations can be efficiently computed with the combination of parallelization and batching strategies. As a consequence, the efficiency of the evaluations depends on the node’s load, and the distribution of evaluations among the nodes must account for this dependence. In this study, we propose an approach to integrate an efficient scheduling of evaluations on heterogenous multi-core nodes in PersEUD. The proposal has been extensively tested on eight clusters, with nodes of three different micro-architectures. The test data set consisted of three head and neck patients treated with IMRT using nine beams. The results indicate that exploiting the cluster appropriately leads to a substantial acceleration of the computation involved in the planning based on PersEUD. This result facilitates the practical implementation of PersEUD in clinical settings.