Embedding Artificial Intelligence (AI) onboard satellites is becoming increasingly important in the field of SpaceTech where radiation hardened Commercial-Off-The-Shelf (COTS) hardware accelerators are becoming popular to run machine learning and deep learning workloads for benefits like optimizing transmission bandwidth utilization and real-time insights. In current practice, designing such ML/DL models requires experts in neural network design, geo-spatial imaging, and embedded systems. Additionally, the task of migrating such models to a different hardware accelerators platform typically requires significant effort to design and re-train the model. This paper shows how a Zero-Shot heuristic can be used to speed-up reinforcement learning based Neural Architecture Search (NAS) to generate tiny but accurate multi-objective models for cloud cover detection. We achieve a 15x reduction in search time, improved latency and energy consumption, to find task and hardware specific model as compared to an handcrafted model.