Feature pyramids have delivered significant improvement in object detection. However, building effective feature pyramids heavily relies on expert knowledge, and also requires strenuous efforts to balance effectiveness and efficiency. Automatic search methods, such as NAS-FPN, automates the design of feature pyramids, but the low search efficiency makes it difficult to apply in a large search space. In this paper, we propose a novel search framework for a feature pyramid network, called AutoDet, which enables to automatic discovery of informative connections between multi-scale features and configure detection architectures with both high efficiency and state-of-the-art performance. In AutoDet, a new search space is specifically designed for feature pyramids in object detectors, which is more general than NAS-FPN. Furthermore, the architecture search process is formulated as a combinatorial optimization problem and solved by a Simulated Annealing-based Network Architecture Search method (SA-NAS). Compared with existing NAS methods, AutoDet ensures a dramatic reduction in search times. For example, our SA-NAS can be up to 30x faster than reinforcement learning-based approaches. Furthermore, AutoDet is compatible with both one-stage and two-stage structures with all kinds of backbone networks. We demonstrate the effectiveness of AutoDet with outperforming single-model results on the COCO dataset. Without pre-training on OpenImages, AutoDet with the ResNet-101 backbone achieves an AP of 39.7 and 47.3 for one-stage and two-stage architectures, respectively, which surpass current state-of-the-art methods.