Social networks are sets of individuals who communicate to each other based on a specific purpose depending on the type of social network. This communication may be friendship, business or scientific cooperation or other kinds of relations. Nowadays, social networks are very popular in the entire world and this popularity makes them suitable for large scale advertisements. Relationships between different users in a social network increase the amount of influence on each user such that related users will have similar interests and activities. One of the important problems in the area of social network is influence maximization problem, i.e. identifying a set of key nodes in a social network that maximizes the influence spread. This problem has gained tremendous attention in recent years. One application of such problem is time-bounded influence maximization for viral marketing. While a number of algorithms exists that give satisfactory performance for influence maximization in large networks, time-bounded influence maximization still remains an open problem. In this thesis, we introduce a new multi-objective optimization based approach for influence maximization considering two objectives maximizing influence and minimizing diffusion time. We adapted NSGA-II algorithm, and in order to make the running time of the algorithm feasible for optimization over large networks, we developed two heuristics for the computation of influence spread and diffusion time of sets of users. In our algorithm, at the end of the optimization phase, the influence of resulting sets of nodes from NSGA-II algorithm is evaluated at constrained time bounds and the final optimal set of nodes under imposed time bounds are given as output which gain higher influence compared to existing algorithms at specific time bounds. Keywords : Influence maximization, Diffusion model, Multi objective optimization, Social networks