Variational quantum algorithms (VQAs) are hybrid approaches between classical and quantum computation, where a classical optimizer proposes parameter configurations for a quantum parametric circuit which is iteratively sampled. The overall performance of the algorithm depends on how the classical optimizer tunes the parameters of the quantum circuit. Several gradient-free and gradient-based approaches have been proposed in the literature to face this task. Estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is updated and sampled at each generation to optimize a cost function. EDAs have shown to be able to achieve good solutions in a reasonable computation time for different optimization problems, and thus, we believe that this algorithm can be a good option to overcome VQAs challenges such as the Barren plateaus phenomenon. In this paper, we study the use of three different EDAs, characterized by different probabilistic model complexities, to tune the parameters of two different VQAs to solver the Max Cut problem and to a VQA to simulate the behaviour of a molecule. Three EDA variants are compared to some state-of-the-art optimizers widely used for this task. Our results show statistical significant improvement of the EDA variants compared to different optimizers, and identify the VQAs characteristics that best fit to each EDA type. We also perform an analysis of the main EDAs hyper-parameters.