Neural Architecture Search (NAS), which allows for automatically developing neural networks, has been mostly devoted to performance on a single metric, usually accuracy. New approaches have added more objectives, such as model size, in order to find networks suitable for resource-constrained platforms. SpArSe  is a multi-objective Bayesian optimization framework for automatically developing image classification convolutional neural networks (CNNs) for micro-controller units (MCUs). In this work, we first implement SpArSe and modify it to reduce search time, obtaining similar results regarding accuracy, model size, and maximum working memory but in less optimization time. Moreover, we extend the search space to include recurrent neural networks (RNNs) and add an inference latency objective for time-constrained tasks. Finally, we test our implementation in a gesture recognition task obtaining better results than previous manually tuned approaches for size and performance metrics, which validates the approach and its utility.