Generative design is an optimization process that is well-suited for various applications in fluid power, including untethered assistive technology exoskeletons powered through hydraulics. While generative design is capable of improving factors such as efficiency, system weight, and surface temperatures, there are currently no solutions that can address these factors simultaneously. The long-range goal of this research is to develop a multiphysics generative design process that combines solid mechanics, fluid mechanics, and heat transfer into a single algorithm to produce designs for high-pressure hydraulic systems that also provide structural support against external loads and passive cooling all in a single integrated structure. To create a generative design algorithm, a Python pipeline was constructed to interface with existing software applications to iterate through geometry creation, meshing, finite volume method, and sensitivity analysis. The pipeline was validated using a simplified case study of pressurized fluid flow through a pipe with a 90-degree bend where the flow path was modified between a fixed inlet and outlet to reduce pressure drop by 37.2±0.4%, corresponding directly to a reduction in battery size and, therefore, system weight. Future work will use multiphysics sensitivity analysis and machine learning to inform the iterative geometry refinement.