Recently, computer modeling and simulation of human movements has attracted many researchers in the field of neuroscience and biomedical engineering. Understanding the strategies of Central Nervous System (CNS) in planning and controlling the movements plays an increasing role in the improvement of rehabilitation protocols, sport techniques, and the recognition of motor disorders. In this case, the present study deals with the computational modeling of the CNS’s performance in planning the Sit-to-Stand transfer as a challenging task. The approach is based on the hypothesis that the CNS may decompose the complicated movements into several simpler sub-tasks (phases of motion). According to this hypothesis and as a novel approach, the constructive basic patterns of movement in each phase of motion are extracted from the recorded data, and compared to the minimum jerk patterns. It is shown that the CNS applies a minimum angle jerk policy when plans the motion of each phase. Also, by proposing a new optimization based model, we suggest that unlike the previous approaches, considering appropriate cost functions corresponding to each phase of motion provides a better description of optimality in complicated movements. In fact, it seems that the CNS considers different strategies to plan different phases of motion. On the other side, the ability of CNS is generating a large repertoire of actions under various conditions reveals that it does not need to go through optimization procedures to plan every single motion. It should be mentioned that optimization imposes heavy computational burdens to the CNS. According to this fact, a novel modular and hierarchical movement planner (MHMP) is developed to describe the function of CNS in planning the multi-phase tasks under different environmental conditions. The MHMP is made of four functional parts including the kinematic estimator modules, a time estimator module, a gating module and a movement element based network. These parts enable the model to plan the movements under unknown conditions by the use of past experiences. The performance of MHMP is evaluated both with the optimization based model, and the empirical captured motions. It is shown that the modular features of the model, as well as the concept of motion decomposition increase the ability of MHMP in planning the real-time and optimal movements under different environmental conditions. Sufficient accuracy of the model, low computational requirements, as well as the behavioral and neurophysiological supports make the model to be considered as a suitable candidate to computationally interpret the function of CNS in planning the tasks. Based on the concepts of movement decomposition, definition of different objective functions for each phase of the movement and the structure of the MHMP, this work is ready to be used for humanoid robots, rehabilitation robots or any other robotic applications. Keywords: Movement planning, Optimization based model, Sit-to-Stand, Motion decomposition, Modular and hierarchical structure.