In the resource-constrained workflow, a task is usually decomposed into a set of dependent subtasks. Since resources are limited, the task decomposition and planning should be optimized according to the specific executing environment. Based on the software component concepts, in this paper, a task is defined as a set of dependent task units, and the subtasks are clips of a task, in which all of the task units can be executed in a single limited resource. This paper presents a task decomposition and planning model for the resources-constrained workflow environment, by which the task decomposition and planning can be abstracted to a combinatorial optimization problem whose objective is to minimize executing and communicating overheads. In order to solve this problem, a discrete particle swarm optimization algorithm is proposed and the simulation experiments prove the effectiveness of the algorithm.