This thesis discusses applying AI planning tools for generating plans for manual operations. Expertise of motion economy domain is used to select good plans among feasible ones. Motion economy is a field of industrial engineering, which deals with observing, reporting and improving manual operations. Motion economy knowledge is organized in principles regarding the sequences and characteristics of motions, arrangement of workspace, design of tools etc. A representation scheme is developed for products, workspace and hand motions of manual operations. Operation plans are generated using a forward chaining planner (TLPLAN). Planner and representation of domain have extensions compared to a standard forward chaining planner, for supporting concurrency, actions with resources and actions with durations. We formulated principles of motion economy as search control temporal formulas. In addition to motion economy rules, we developed rules for simulating common sense of humans and goal-related rules for preventing absurd sequences of actions in the plans. Search control rules constrain the problem and reduce search complexity. Plans are evaluated during search. Paths, which are not in conformity with the principles of motion economy, are pruned with motion economy rules. Sample problems are represented and solved. Diversity of types of these problems shows the generality of representation scheme. In experimental runs, effects of motion economy principles on the generation of plans are observed and analyzed.