Energy consumption in five-axis machining can be extremely large, especially for processing large-sized parts such as blades and dies, which often take hundreds of hours to machine. Once the specific machine tool configuration is given, one important objective in planning a five-axis machining operation is to reduce the total amount of energy consumption, which is not only particularly pertinent for mass production type operations but also plausible in today's energy-conscientious environment. While past research in this subject has been mainly focusing on finding better machining or processing parameters such as the cutting depth, feed rate, spindle speed, etc., in this paper, we study the fundamental problem of how to plan a five-axis tool path for machining an arbitrary free-form part surface on a given type of machine tool so that the total amount of energy consumption can be reduced, as compared to a traditional tool path, with everything else – e.g., the cutting depth, feed rate, spindle speed, maximum scallop-height, etc. – being equal. Towards this goal, an algebraic model called machine dependent energy potential field is formulated on the part surface, which encapsulates the specific power demand at any cutter contact point and along any feed direction. With this potential field, a simple tool path generation method is proposed that strives to find a good balance between the best fitting to the potential field (to minimize the total energy consumption) and the desired streamline patterns of the tool path. Experiments of the proposed method are carried out in both computer simulation and physical cutting, and the results are compared with that of some popular tool path generation methods such as the iso-scallop height method. The remarkable more than 25% savings in total consumed energy by the proposed method confirm the original motivation – designing better tool paths adaptive to the given specific machine tool is a powerful approach to reducing the total consumed energy, perhaps in general more effective than the passive selection of machining parameters which often have little room to adjust due to many constraints (e.g., the feed rate is highly regulated by the machine's controller during the cutting).