Differential evolution (DE) is a very powerful and simple algorithm for single- and multi-objective continuous optimization prob- lems. However, its success is highly affected by the right choice of param- eters. Although significant progress has been made in the single-objective realm, the choice of competitive DE parameters for multi-objective prob- lems is still far from being well understood. In particular, authors of suc- cessful multi-objective DE algorithms usually use parameters which do not render the algorithm invariant with respect to rotation of the coor- dinate axes in the decision space. In this work we explore what are the consequences of using such parameters when the problem rotates. and try to establish which parameters offer the more robust setting with respect to rotation invariance. We do this by testing a DE algorithm with various parameters on a testbed of bi-objective problems with various modality and separability characteristics. Then we explore how the performance changes when we rotate the axes in a controlled manner. We find out that our results are consistent with the single-objective theory but only for unimodal problems. On multi-modal problems, surprisingly, param- eter settings which do not render the algorithm rotationally invariant have a consistently good performance for all studied rotations.