Object re-IDentification (reID) is the process of matching the same object across multiple cameras. An approach for object reID is crucial for identifying objects like people, cars, motorcycles etc., in video surveillance systems. 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. This work proposes an approach for automatically designing a deep convolutional neural network, named MNASreID, for motorcycle re-identification task. 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. MNASreID uses Neural Architecture Search (NAS) to search for the backbones of the Deep Neural Network (DNN) model. In addition, the designed search space includes parameters of the architecture and its hyperparameters to find the optimum architecture for the reID task. This work uses Grasshopper Optimization Algorithm (GOA) based NAS to find the optimal DNN model. The outcomes of experiments on two motorcycle datasets demonstrate that our method can automatically find the efficient DNN model for motorcycle reID.