Object re-identification (reID) plays a pivotal role in traffic surveillance systems for matching objects like people, cars, and motorcycles across multiple cameras. This is an active area of research in both industry and academia due to the ever-growing population and need for smart surveillance, public safety, and traffic management. Most current reID methods use deep convolutional neural networks as the backbone that are manually designed, which does not have the optimum settings as the network complexity increases. This paper introduces MNASreID, an automated approach for designing deep convolutional neural networks designed specifically for motorcycle reID. Key contributions include proposing a NAS based optimization framework and designing a comprehensive search space covering backbone architectures and hyperparameters. Grasshopper optimization algorithm used as NAS search strategy to find the optimal DNN model. Experimental results on two motorcycle datasets, MoRe and BPReID, demonstrate MNASreID’s ability to automatically identify efficient DNN models for reID tasks. Comparative evaluation against existing algorithms reveals significant performance enhancements. Specifically, MNASreID achieves a notable improvement of +1.14% and +1.24% in r1 and mAP metrics, respectively, on the MoRe dataset. On the BPReID dataset, it outperforms existing approaches by +26.82% and +29.56% in r1 and mAP metrics, respectively.