In many social networks, the connections between actors are formed because they participate in the same event, such as a set of scholars coauthoring a paper and a person making phone calls or having teleconferences with his friends. Therefore, we propose an event-driven framework for creating network growth models. We also notice that in evolving networks, both the behavior of the whole network and the behavior of nodes evolve over time. For example, we observe in collaborative networks that the growth rates of the communities and the average number of coauthors in papers change as the network sizes increase over time, and researchers' interactions with local groups and remote groups also evolve over time with their experience (degree). These observations motivate us to propose a behavior evolution-aware event-driven locality and attachedness based model to capture the growth dynamics in social networks. Based on some informative metrics of network structure and properties, such as degree distribution, degree-dependent clustering coefficients, and degree-dependent average degree of neighbors, the experiments suggest that our model can better characterize the growing process and simulate important network structures observed in real networks than other non-event driven and non-behavior aware models.