In this paper we study a population of individuals in a simulated artificial environment. These individuals have a "body" as well as a "mind", i.e., some of their features effect their "physical" properties, like speed and strength, while other features influence their "mental" preferences and choices in interacting with the environment and other agents. We compare two approaches to adapting the minds of individuals. In approach 1, the bodies and the minds develop through evolution, while in approach 2 only the bodies evolve and the minds are adapted by lifetime-learning. The results indicate that the evolutionary approach is able to sustain larger and more stable agent populations as well as maintain a higher degree of individual success compared to the lifetime learning approach. Furthermore, quite unexpectedly, the method used for mental development has a strong effect on the development of the physical features within the very same environment: The individuals' bodies evolve to completely different segments of the physical feature space under the two regimes.