Spiking neural networks (SNNs), inspired by biological brains, use discrete spikes for communication, offering potential advantages in energy efficiency and temporal processing. These properties make them attractive for low-power, real-time control, but optimizing their structure and parameters is challenging. This work investigates the impact of spatial embedding on recurrent SNN performance and efficiency in continuous control tasks. We evolve SNNs with neurons positioned in a 3D Euclidean space, where connection probabilities and strengths decrease with distance. A genetic algorithm optimizes neuron parameters, connection weights, and network topology. Evaluating various spatial embeddings (none, 1D, 2D, and 3D) across multiple reinforcement learning environments, we find that spatially embedded networks outperform non-embedded counterparts within our framework. We also find that the 2D embedding generally achieves the best performance. Spatial embedding also leads to highly sparse networks, with over 95% of weights being zero. Compared to state-of-the-art deep reinforcement learning models, our evolved SNNs achieve similar performance on simpler tasks using as little as 1.46% of the non-zero weights. A performance gap on more complex tasks suggests the need for future research on larger, spatially embedded networks. These findings show the potential of spatial embedding for designing efficient SNNs for neuromorphic control.