Collaborative Multi-Agent Planning (MAP) under uncertainty with partial observability is a notoriously difficult problem. Such MAP problems are often modeled as Dec- POMDPs, or its qualitative variant, QDec-POMDP, which is essentially a MAP version of contingent planning. The QDec- POMDP model was introduced with the hope that its simpler, non-probabilistic structure will allow for better scalability. Indeed, at least with deterministic actions, the recent IMAP algorithm scales much better than comparable Dec- POMDP algorithms (Bazinin and Shani 2018). In this work we suggest a new approach to solving Deterministic QDec- POMDPs based on problem factoring. First, we find a solution to a MAP problem where the results of any observation is available to all agents. This is essentially a single-agent planning problem for the entire team. Then, we project the solution tree into sub-trees, one per agent, and let each agent transform its projected tree into a legal local tree. If all agents succeed, we combine the trees into a valid joint-plan. Otherwise, we continue to explore the space of team solutions. This approach is sound, complete, and as our empirical evaluation demonstrates, scales much better than the IMAP algorithm.