Computational simulations of a multibody dynamic response are an important tool for the analysis and design of various mechanical systems. While the governing dynamic equations of these systems are well known, the identification of model parameters, especially those associated with joints, can prove difficult and time consuming. Traditionally, experimental methods are used to deduce the physical joint parameters by isolating the joint from the rest of the structure and testing it under static or dynamic loads. An alternative to pure experimental joint-parameter identification is the model-based methods, which rely on finding such parameter values that the predicted dynamic response coincides with that of the real system. As the equations of multibody systems are highly nonlinear, linearization techniques are applied to efficiently deduce the system’s dynamic parameters using modal analysis. Although significant progress has been made in recent years, none of the studies that propose the linearization technique has addressed the effect of multibody system equilibrium-point selection on the accuracy of the parameter-identification procedure. Therefore, here, a new general model-based parameter-estimation method is proposed that minimizes the difference between the experimentally and numerically obtained dynamic system’s natural frequencies. The basic idea of the proposed method relies on the development of an algorithm that identifies the optimal equilibrium point of the linearization for a given multibody system. The equilibrium point is deduced in such a way as to minimize the interplay between the different joint parameters on the system’s natural frequencies. Using the proposed approach it is possible to localize the influence of the individual joint’s stiffness parameters to one particular natural frequency. The presented case study highlights the efficiency of the developed parameter-estimation procedure and with this the importance of a proper linearization equilibrium-point selection for a reliable and accurate parameter-identification process.