There is a large class of problems that can be represented by task decomposition trees which specify alternative sets of variables that must be given values. Any one of these alternatives can be represented as a constraint satisfaction problem. We describe a technique for selecting a task decomposition that results in a constraint problem that can be solved efficiently. This technique takes advantage of the constraint graph textures suggested by work on constrained heuristic search. First, texture-based heuristics developed for the task decomposition tree are used to select alternative subtasks. As each subtask is selected, its variables, and any constraints associated with them, are added to the constraint graph and constraints are propagated. In addition, variables in the constraint graph may be assigned a value when suggested by texture-based heuristics. When a complete decomposition has been selected, constrained heuristic search is used to finish solving the constraint problem. Experimental results suggest that the use of texture-based heuristics for selecting the task decomposition and for deciding when to assign values in a partial constraint graph result in a significant improvement in efficiency and time needed for solving the problem.