Social networks and location based social networks have many active users who provide various kind of data, such as where they have been, who their friends are, which items they like more, when they go to a venue. Location, social network and temporal information provided by them can be used by recommendation systems to give more accurate suggestions. Also, recommendation systems can provide dynamic recommendations based on the users' preferences, such that they can give different recommendations for different hours of the day or different days of the week. In this paper, we propose a recommendation system which considers the users' temporal preference to give dynamic recommendation. The recommendation method uses multi-objective optimization approach and gives point of interest (POI) recommendation using several different criteria, namely past check-in locations, hometown of users, time of check-ins, friendship and influence among users.