Joint inversion strategies for geophysical data have become increasingly popular since they allow to combine complementary information from different data sets in an efficient way. Here, we present a non-linear joint inversion scheme, in which data from different methods are inverted separately and are joined through constrains accounting for parameter relationships. To avoid that the convergence behavior of the inversions is not profoundly disturbed by this coupling, the strengths of the constraints are re-adjusted at each iteration. In contrast to a joint inversion with a fixed parameter relationship, where data is inverted to one common model, this scheme requires no relative weighting of the data sets from different methods. Moreover, we observe that the adaption of the coupling strengths makes the convergence of the inversions much more robust. When we test our scheme with and without adaption on a synthetic 2-D model with seismic tomography, gravity and MT data, the final results with adaption were significantly closer to the true model. Finally, we observe that the adaptive scheme can to some extent handle models with structures for which the assumed parameter relationships are invalid.