Scheduling is a decision-making process and one of the important parts of production planning systems to allocate limited resources to jobs. Hybrid Flow Shop (HFS) scheduling problem has good adaptability with most of the real world problems, which includes innumerable cases of uncertainty of parameters that would influence jobs processing when the schedule is executed. A suitable scheduling model should consider them. Hence, the present study develops a multi-objective Robust Mixed-Integer Linear Programming (RMILP) model to accommodate the problem with the real-world conditions in which due date and processing time are assumed uncertain. The developed model is able to assign a set of jobs to available machines in order to obtain the best trade-off between two objectives including total tardiness and makespan under uncertain parameters. Fuzzy Goal Programming is applied to solve this multi objective problem. Finally, to study the efficiency and verification of the developed RLP model, some instances of different size are generated and solved using GAMS. The experimental results show that the developed model can find a solution to show the least modifications against uncertainty in processing time and due date in an HFS problem.