Representing a physical system with a mathematical model requires knowledge not only about the physical laws governing the dynamics but also about the parameter values of the system. The parameters can sometimes be measured or calculated, but some of them are often difficult or impossible to obtain directly. Never the less, finding accurate parameter values is crucial for the accuracy of the mathematical model.Estimating the parameters using optimization algorithms which attempt to minimize the error between the response from the mathematical model and the real physical system is a common approach for improving the accuracy of the model.Optimization algorithms usually require information about the derivatives which may not always be easily available or which may be difficult to compute due to, e.g., hybrid dynamics. In such cases, derivative-free optimization algorithms offer an alternative for design and parameter optimization.In this paper, we present an implementation of derivative-free optimization algorithms for parameter estimation in the JModelica.org platform. The implementation allows the underlying dynamic system to be represented as a Functional Mock-up Unit (FMU), and thus enables parameter optimization of models exported from modeling tools compliant with the Functional Mock-up Interface (FMI).