Modelling the dynamics of epidemics helps proposing control strategies based on phar-maceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccina-tion, etc). Hand-designing such strategies is not trivial because of the number of pos-sible interventions and the difficulty to predict long-term effects. This task can be castas an optimization problem where state-of-the-art machine learning algorithms such asdeep reinforcement learning, might bring significant value. However, the specificity ofeach domain – epidemic modelling or solving optimization problem – requires strong col-laborations between researchers from different fields of expertise. This is why we intro-duce EpidemiOptim, a Python toolbox that facilitates collaborations between researchersin epidemiology and optimization. EpidemiOptim turns epidemiological models and costfunctions into optimization problems via a standard interface commonly used by optimiza-tion practitioners (OpenAI Gym). Reinforcement learning algorithms based on Q-Learningwith deep neural networks (dqn) and evolutionary algorithms (nsga-ii) are already im-plemented. We illustrate the use of EpidemiOptim to find optimal policies for dynamicalon-off lock-down control under the optimization of death toll and economic recess using aSusceptible-Exposed-Infectious-Removed (seir) model for COVID-19. Using EpidemiOp-tim and its interactive visualization platform in Jupyter notebooks, epidemiologists, op-timization practitioners and others (e.g. economists) can easily compare epidemiologicalmodels, costs functions and optimization algorithms to address important choices to bemade by health decision-makers. Trained models can be explored by experts and non-experts via a web interface.