Wave energy converter (WEC) design optimization has traditionally focused on minimizing the Levelized Cost of Energy (LCOE) or similar proxies. However, this approach overlooks the realistic drivers of energy system planning, where capacity installation decisions are made to minimize the overall system cost. Grid system cost does not necessarily align with LCOE due to the complex temporal and spatial relationship between energy generation and grid demand. Additionally, conventional WEC optimization neglects broader electrification goals, where the reduction of lifetime equivalent carbon dioxide emissions is the key driver of climate change mitigation. To bridge this gap, the authors previously proposed a system-level techno-economic and environmental WEC optimization framework that integrates capacity expansion modeling (CEM) and life cycle analysis (LCA) into the design objective. This approach provides a more comprehensive assessment of wave energy’s net value proposition beyond conventional cost metrics. In this work, we implement this methodology within a new open-source multidisciplinary design optimization framework. Our implementation leverages the GenX CEM, the PowerGenome energy data interface, the Idemat LCA dataset, and the MDOcean wave energy converter model. A surrogate model of the CEM reduces computation time compared to the naive CEM-in-the-loop approach. We present preliminary optimization results for the Reference Model 3 (RM3) WEC, demonstrating the impact of optimizing for new value-driven economic and environmental system metrics compared to the standard LCOE. For the scope of emissions controllable in the early device design phase, the environmental objective aligns well with the economic one, indicating that design-for-environment techniques may not be relevant until later in the design process. Meanwhile, the grid system economic objective is hypothesized to favor larger WECs in scenarios with winter generation deficits, such as the U.S. northeast with electrified heat loads, where the value of capturing energetic winter sea states outweighs the cost of surviving them. On the other hand, systems with storage constraints should favor smaller WECs, where the avoided storage cost due to more consistent power generation outweighs the penalty in absorbed power. Finally, we discuss the broader implications of these findings for future WEC design optimization priorities.