In this paper, a multi-period multi-echelon reverse logistics network design problem under high extent of uncertainty is addressed. We first formulate and then solve the multi-period network design model using the cloud-based design optimization framework which ensures to: (1) handle high number of uncertain factors; (2) propose alternative solution to traditional approaches; (3) provide a robust solution which strengthens decision makers against unexpected situations. Finally, applicability of the presented approach is tested through a dataset of e-waste reverse logistics network.