Problems with multiple constraints can be tricky, but breaking them down into smaller, manageable parts makes them easier to solve. Start by analyzing each constraint independently and coming up with a solution that satisfies each one. Then, think about how to combine these solutions without violating any of the constraints. Sometimes dynamic programming or a greedy approach can help here, while in other cases, you may need to use backtracking or branch-and-bound techniques to explore all possible combinations. The key is to methodically address each constraint while keeping the overall solution efficient.