In a multi-modal multi-objective route planning problem, the main purpose is finding an optimal route between the origin and destination, which is a combination of multi-transportation modes, pairs by considering multi-fitness function. Most of multi-objective problems are solved by assigning a weight to each objective function and using a linear averaging of the objectives as a distinct objective function. These methods have some weaknesses such as inability in searching the problem space and a need to normalize the objective functions. Therefore, in this paper, a non-dominated sorting genetic algorithm (NSGA-II) has been used to solve the multi-modal multi-objective routing problem. This algorithm proposes a set of non-dominated routes that has no absolute superiority to each other. Finally, the optimal route was determined using TOPSIS method from this set. The intended objective functions in this research are the lowest number of changes in transportation means, fare and time during the path. Moreover, five transportation modes including subway, taxi, bus, BRT, and walking transportation modes have been considered as means of transportation inside the mentioned network. This algorithm was implemented in a part of Tehran transportation network and results showed that the proposed NSGA-II algorithm proposed a better route in 89% and 87% of the routing cases than those of the genetic and the simulated annealing algorithms respectively.