Due to the global ramp-up of electromobility, the need for lithium-ion batteries is constantly growing. This causes various challenges for the automotive industry. Especially, the procurement of raw materials for battery production is associated with many difficulties. For instance, the availability of some of these raw materials is strongly concentrated in individual regions worldwide, which leads to dependencies on certain regions or suppliers and incalculable price fluctuations for many automotive companies. To counter such problems, many supply chain actors seek to integrate end-of-life strategies (reuse, refurbishment, repurposing, and recycling). The resulting closed-loop supply chains can extend life cycles, use raw materials more efficiently, and can help to reduce overall greenhouse gas emissions. In this context, the question arises regarding how the battery market will develop in the coming years, including the return rate of used batteries and how the capacity build-up of production and recovery of lithium-ion batteries should be designed to fulfill the demand. The objective of this research is to develop a method that allows decision-makers to determine the timing and dimensions at which capacities of the production and recovery processes need to be built up to design a long-term stable supply chain. Therefore, the optimal capacity expansion strategy along the closed-loop supply chain must be identified. We designed a two-step approach to derive an economically and environmentally beneficial capacity expansion strategy. First, a life cycle simulation model will be used to determine the demand progression, return rate, and distribution of the different end-of-life strategies of batteries based on the resulting greenhouse gas emissions. Following, we determine the total capacity requirements for the production and recovery plants. Second, the results are used within a mathematical optimization model that supports decision-makers to determine the optimal capacity expansion strategy, considering, for example, economies of scale. Only by linking these two models and developing the two-step approach is it possible to simulate the dynamic changes in the system environment and develop an optimal capacity expansion strategy. To quantify and illustrate the results of this two-step approach, a case study was conducted between the development of the German and Japanese battery markets. Comparable economic conditions characterize both countries, but very different strategies and preferences of the population regarding the market penetration of electric vehicles. The analysis shows that the total emissions for the production and recovery processes are higher in Germany than in Japan. However, when considering the emissions per battery, the values in Japan are higher than in Germany. The results also indicate that it would be economically more reasonable for the German market to make high investments in large plants along the value chain at an early stage. In contrast, based on the historically much slower ramp-up, it would make more sense to build small plants in Japan and always stay as close as possible to current demand.