This article reviews existing work and future opportunities in neuroevolution, an area of machine learning in which evolutionary optimization methods such as genetic algorithms are used to construct neural networks to achieve desired behavior. The article takes a neuroscience perspective, identifying where neuroevolution can lead to insights about the structure, function, and developmental and evolutionary origins of biological neural circuitry that can be studied in further neuroscience experiments. It proposes optimization under environmental constraints as a unifying theme and suggests the evolution of language as a grand challenge whose time may have come.