Estimation of Distribution Algorithms (EDAs) are a set of optimization techniques that have been successfully applied to different kinds of problems. In this paper, we deal with the creation of multivariate calibration models in quantitative chemistry. For this purpose, we use parallel implementations of two EDAs (EBNABIC and UMDA), using different approaches to create a calibration model using data obtained from controlled reactions. Once the calibration model has been trained, it can be used to predict initial concentrations for some species taking part in new reactions. The results show that these new approaches are able to obtain good-quality calibration models. Moreover, the use of parallel algorithms allows researchers to complete experiments faster and to study a wider set of alternative solutions.