A new method to learn brain effective connectivity (EC) networks by parallel searching of double firefly populations in order to identify high-quality brain EC network from functional magnetic resonance imaging (fMRI) data. The double population optimization was used to learn brain EC networks. The initial population was divided into an elite population and a common population. Then the brain EC networks were gradually constructed through the directional movements of the elite population and the random movements of the common population. The population sizes of the elite population and the common population were dynamically adjusted in the process of network constructions, and the information exchange between the two populations was realized by using a population migration operation. An adaptive updating mechanism based on a diversity measure was used to dynamically update the two populations after a certain number of evolution iterations. The experimental results on many simulated datasets verify that the new algorithm has obvious advantages on the whole performance compared with other algorithms, and shows the potential practicability of the new algorithm on real datasets.