The paper presents an open-source Python tool for parameter estimation in FMI-compliant models, called ModestPy. The tool enables estimation of model parameters using user-defined sequences of methods, which are particularlyhelpful in non-convex problems. A user can start estimation with a chosen global search method and subsequently refine the estimates with a local search method. Several methods are available already and the tool’s architectureallows for easily adding new ones. The advantages of having a single interface to multiple methods and using them in sequences are highlighted on a case study in which the parameters of a Modelica-based gray-box model of a building zone (nonlinear, multi-output) are estimated using 9 different combinations of methods. The methods are compared in terms of accuracy and computational performance.