Effectively leveraging the characteristics and capabilities of spiking neural networks for neuromorphic computing systems for real-world applications is an important challenge, especially as we target applications that have severe energy constraints. In this talk, I will overview the use of evolutionary optimization to design spiking neural networks for neuromorphic deployment. I will specifically highlight several real-world applications, such as radiation detection, internal combustion engine control, and autonomous race car control. I will discuss the advantages of using evolutionary optimization to design spiking neural networks for neuromorphic systems, including the ability to perform multi-objective optimization for energy efficiency and resiliency.