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The existing models and algorithms for predicting human reachable workspaces do not allow users to specify important performer and task parameters, such as body weight and muscular strengths of reach performer and hand-held object's weight. This makes it difficult to consider individuals with unique physical characteristics (e.g., obesity, muscle strength deficiencies and injuries) and many common tasks involving hand-held objects during reach analyses. To address this, this study presents a novel, biomechanically based workspace generation algorithm. Given a set of input data specified in terms of body dimensions, joint ranges of motion, body joints muscular strengths, gender, body weight of a reach performer and a hand-held load weight, the algorithm generates the corresponding reachable workspace. The algorithm combines the existing human figure based modeling approach with empirically obtained biomechanical data and established biomechanical models and constraints.