Collective migration in animal groups is often guided by distributed leader agents that influence their peers by modulating their motion behaviors. Distributed leadership is a promising navigation strategy for artificial swarms, but designing the leaders’ controllers is difficult due to the swarm’s collective emergent behaviors. The presented research formulates a swarm control strategy that leverages trained leaders to influence the collective’s trajectory in spatial navigation tasks. A swarm’s decentralized architecture is advantageous for scalability and robustness, but poses challenges in motion coordination, because influence and information propagate through local interaction networks, rather than global channels. A neuro-evolutionary learning-based control method is presented in which a subset of a swarm is trained to influence motion behaviors. The leadership control strategy is applied to a rally task with varying swarm sizes and leadership percentages. Increasing the leader representation improved task performance, but the performance increase was negligible for a leader-swarm ratio of 16% or greater. The learned behaviors were different with high and low leader percentages. The leaders moved quickly when the swarm had a higher percentage of leaders and slowly when the percentage was small.