As a Demand Response (DR) based working mode, the integrated scheduling of energy supply and demand provides an effective approach to improve economic and environmental benefits for Microgrids (MGs). However, it is still a challenging issue to cover uncertainties caused by intermittent renewable energy and random loads while optimizing multiple objectives in economy and environment. To tackle this issue, an integrated scheduling approach for MGs is proposed based on robust multi-objective optimization. Firstly, load shifting in a finite time is introduced to express an acceptable DR program for industrial customers. A minimax multi-objective optimization model is formulated to seek the minimum operation costs and emissions under the worst-case realization of uncertainties, which are captured by the robust sets with budgets of uncertainty. Secondly, a strong duality based model transformation method is implemented to cope with the strong coupling and nonlinearity in the proposed formulation. Also, Multi-Objective Cross Entropy (MOCE) algorithm is adopted to solve the reconstructed model for simultaneously optimizing all the objectives. Finally, detailed comparative experiments are conducted in problem level, model level and algorithm level. The simulation results show that the proposed scheduling approach can effectively attenuate the disturbances of uncertainties as well as achieve optimal economic and environmental benefits, compared with single-objective robust optimization scheduling approaches and deterministic multi-objective optimization scheduling approaches. Meanwhile, the validity and effectiveness of the robust multi-objective optimization approach for the MG integrated scheduling problem under uncertainty are confirmed. (C) 2017 Published by Elsevier Ltd.