The building industry is responsible for a large amount of CO2 emissions. With an estimated 11.7 GT in 2020, the building industry emitted 36% of the worldwide CO2 emissions (Bertin et al., 2022). This results in the need to efficiently use the current material supply. A way to achieve this is by transitioning from a linear economy to a circular economy. Within the principles of the circular economy, materials are kept in use by creating closed loops. This results in the prevention of waste. Examples of strategies that comply with the circular economy are repairing, reusing and recycling of materials or components (Brütting et al., 2019). Recycling of steel components has become common practice over the years. Reusing steel components is less common. Reusing structural steel components can reduce overall emissions. This is because it excludes the highly impactful manufacturing phase (Yeung et al., 2016). Structural steel is suitable for reuse because members are often connected by reversible connection principles. Additionally, the steel industry has a high level of standardization and prior to reuse the structural integrity can be easier guaranteed through testing or available certification in comparison to concrete (Fivet & Brütting, 2020). When talking about the efficient use of materials also the gridshell topology is interesting to mention. Because of the double-curvature a gridshell is able to span large areas with less structural mass (Schober, 2015). Both the use of gridshell topologies and the reuse of steel are combined in this research. The question this research tends to answer is formulated as follows: “How can computational optimization contribute to the design of gridshell structures consisting out of a finite stock of reclaimed steel beam members with the goal to improve the eco-performance calculated in embodied greenhouse gas emissions?” From the literature different forms of structural optimization methods were found that related to the gridshell structural topology. In the literature sizing-, shape-, and topology optimization are mentioned (Li, 2018). Sizing- and topology optimization are most relevant within the scope of this research. Within this research sizing optimization is limited to stock-constrained optimization. This form of optimization optimizes according to a finite stock. Topology optimization can be divided into rationality-based optimization and structural-based optimization. Within the research of Brütting (2020) optimization of structures out of a finite stock is conducted according to the scenarios of deconstructing and reusing steel and the new production of steel. From additional research another scenario was identified. This is a scenario where a third party or a party via a material database offers their stock. Within this research this scenario is called the stockpile scenario...