In this paper, we will use agent-based modeling with the aim of simulating customer purchase processes in competitive market environments. These simulations will help to understand how customer behavior is affected by different social network topologies and the acquisition success of the products offered. We will extend a well-known agent-based model by incorporating an awareness customer behavior into it to make it closer to reality. In this way, individuals will not initially have a complete knowledge about all the products but they will gradually acquire it through a word-of-mouth process within the social network. Additionally, we will use genetic algorithms to generate automatic viral marketing strategies based on social network analysis metrics. We will compare the marketing results of the combined strategies and their impact based on different types of networks and the number of influential individuals. Finally, we will show that word-of-mouth evolves slower due to the awareness filter and that the genetic algorithm is able to find good solutions for targeting the most influential members of the market according to social network information.