Multiobjective optimization is widely used in applications for modeling and solving complex decision-making problems. To help resolve computational and cognitive difficulties associated with problems which have more than 3 or 4 objectives, we propose a decomposition and coordination methodology to support decision making for large multiobjective optimization problems (MOPs) with global, quasi-global, and local variables. Since the MOPs are decomposable into subproblems, the methodology allows the decision maker (DM) to quantify tradeoffs between the subproblems rather than only between specific objectives associated with them. To coordinate the subproblems, we extend the theory of achievement scalarizing functions which allows for the subproblems to be autonomously coordinated without the DM’s participation. However, we do not totally exclude DMs, by proposing a hybrid coordination method where autonomous coordination is used to aid them in an interactive procedure to explore the subproblem tradeoffs. Finally, we demonstrate the effectiveness of our work on a humanitarian aid case study.