Semantic segmentation is an important activity in satellite image analysis. The manual design and development of neural architectures for semantic segmentation is very tedious and can result in computationally heavy architectures with redundant computation. Neural architecture search (NAS) produces automated network architectures for a given task considering computational cost and other parameters. In this work, we proposed an attention-based neural architecture search (ANAS), which uses attention layers at cell levels for effective and efficient architecture design for semantic segmentation. The proposed ANAS has achieved better results than previous NAS-based work on two benchmark datasets.