In industry, workstations need to be optimized in order to reduce work-related musculoskeletal disorders (WMSD) [1]. To avoid iterations over costly physical mock-ups, digital human modelling (DHM) tools can be used to assess the ergonomic risks on virtual humans performing the industrial tasks. In this context, the anthropometric variability of workers must be considered. A given workstation may be particularly suited for a human with a specic combination of anthropometric variables (height, weight, ...). Conversely, a task may be infeasible if some of these variables lie outside a specic interval. Knowing this in advance can help build workstations better suited to a diversity of workers. How can we study the suitability of a workstation activity for dierent morphologies? In this work, we adress this problem by using a quality-diversity algorithm (Multi-Task MAP-Elites [3]) on physics-based virtual humans performing industrial activities. More precisely, a drilling activity is replayed on a population of virtual humans with a torque controller based on quadratic programming [4]. Here, the use of physics simulation guarantees the dynamic consistency of the virtual human postures and torques, which helps with estimating biomechanical-based ergonomics indicators [2]. The quality-diversity algorithm explores dierent ways of performing the activity, by searching for controller parameters and initial postures that maximize the ergonomics while completing at best the activity. As a result, the method outputs a map that shows the best performance for each morphology. The degree of uniformity of the map gives an indication of whether some morphologies are better suited for the workstation activity, and whether its design needs to be adapted to a broader population. [1] S. Bevan. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Practice and Research: Clinical Rheumatology, 29(3):356373, 2015. Publisher: Elsevier Ltd. [2] P. Maurice. Virtual ergonomics for the design of collaborative robots. PhD thesis, 2015. [3] J.-B. Mouret and G. Maguire. Quality diversity for multi-task optimization. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pages 121129, Cancún Mexico, June 2020. ACM. [4] J. Salini. Dynamic control for the task/posture coordination of humanoids : toward synthesis of complex activities. PhD thesis, 2013. Publication Title: HAL.