Nowadays, the population growth has caused the world, all over, to face irrecoverable life/financial losses due to natural/unnatural (man-made) disasters. The humanitarian logistic can, as an important crisis management activity, play a vital role in rescuing people’s lives, transferring the injured/affected people from the affected area to emergency centers, evacuating the homeless, and meeting people’s needs in disaster conditions. In this paper, have been proposed a multi-objective mathematical model for the humanitarian supply chain design problem that minimizes: 1) total number of the injured not transferred to hospitals and total number of the homeless not evacuated from the affected area, and 2) total unmet relief commodity needs. In this model, demand and travel time have been considered as uncertain and robust counterpart model with “box and polyhedral” uncertainty sets have been developed to model uncertainties. The results obtained by the solving deterministic and robust models show that the under each degree of conservatism level, pareto optimal solution were generated. Also, under nominal data and each degree of conservatism level, the robust model performs worse than the deterministic model.