Every day, people are presented with tasks that are completed with very little mechanical effort such as turning a door knob, turning a screw driver, and grabbing a cup to move it to a new location and/or orientation. These tasks are often overlooked in the mechanical study of human movement due to the fact they carry with them very little biomechanical costs or effort. However, from a cognitive standpoint, these tasks carry high complexity. For example, the simple task of grabbing a cup and flipping it over is very easy mechanically and can be effortlessly achieved by a two year old. However, it is impossible to predict or simulate this motion without major intervention in the form of explicit constraints defining the task. The model itself cannot decide in which orientation the hand should assume in order to grasp the object. Also, it cannot decide where on the object the hand should be placed. These aspects must be assumed by the researcher and constrained in the formulation. In other words, digital human models, such as optimization-based motion prediction models, are unable to plan actions. This implies that for each task, a unique optimization formulation is needed in order to predict the motion/posture needed to complete each task. This paper presents a new method for task planning prediction within optimization based posture and motion prediction. It provides a new single optimization formulation that allows for the prediction of multiple unique manual manipulation tasks. The method is based on observations made from experimental studies on cognitive motor planning.