Work-related musculoskeletal disorders (WMSDs) are among the most common injuries associated with industrial tasks. Repetitive tasks are a major WMSDs risk factor, because they load the same human joints over and over again. Collaborative robots can be used to induce movement variability in highly repetitive co-manipulation tasks by changing the position of the co-manipulated object through time, thereby distributing the physical load over different body parts and reducing fatigue accumulation. This is even more beneficial when long-term consequences of the robot actions are considered. However, selecting the optimal action within the continuous robot workspace is not compatible with time constraints imposed by online planning in highly repetitive tasks, especially when the planning horizon increases. In this work we therefore propose an approach to automatically extract a set of actions from the continuous workspace, that combines two properties: planning speed (i.e. reduced number of actions in the set), and ability to induce a variety of fatigue distributions over the different human joints. The proposed approach combines a digital human simulation to estimate the fatigue induced by possible actions, with a repeated short-term planning (greedy-based selection approach) phase that explores the fatigue space and simultaneously identifies optimal actions from a large space for each visited state. By retaining actions used in the short-term planning, this process allows to extract a subset of relevant actions. We evaluate our approach in a simulated co-manipulation scenario, and show that the resulting action set robustly outperforms action sets extracted with benchmark methods, both in terms of planning time and human fatigue mitigation.