Prediction is an important foundation of cognitive and intelligent behavior. Recent advances in deep learning heavily depend on prediction, in the form of self-supervised learning based on prediction and reinforcement learning (reward prediction). However, how such predictive capabilities emerged from simple organisms has not been investigated fully. Prior works have shown the relationship between input delay and predictive function to compensate for such delay. In this paper, we investigate other key factors that may contribute to the emergence of predictive behavior in evolving neural networks. We set up a delayed reaching task with a two-segment articulated arm. The arm is controlled to reach a moving target, where the target’s coordinate information is received with a delay. Following our previous work, we introduced a tool to extend the reach, when the target is beyond the arm’s reach. In this task, without predicting the trajectory of the moving target, the controller cannot reach the target. For the controller, we used the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. Our results indicate that an important (auxiliary) fitness criterion for the emergence of predictive behavior is that of reduced energy usage (in the form of economy of motion). Further analysis shows that the number of recurrent loops correlates with target reaching performance, but more strongly so with the energy constraint. We expect our findings to lead to further investigations on the role of energy constraints on the evolution of predictive behavior.