<p>Deep learning has recently emerged to address the non-intrusive load monitoring (NILM) problem. Existing deep learning methods for NILM often rely on manually designed models that struggle to efficiently capture temporal and spatial patterns in smart meter data. These models also tend to overlook computational constraints. This paper introduces MOGSM-Net, a multi-label deep learning model for NILM designed automatically through neural architecture search (NAS). MOGSM-Net combines convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms in a lightweight hybrid architecture. It employs a multi-label classification head with an asymmetric loss function to handle the highly imbalanced data inherent to the NILM problem. To discover high-performance and lightweight MOGSM-Net architectures, we developed a multi-objective genetic search algorithm. Performance evaluation using two publicly available NILM datasets shows that MOGSM-Net outperforms existing state-of-the-art methods while remaining computationally efficient, making it a promising solution for NILM.</p>