Abstarct: In real conditions some situations may happen that single objective mathematical models can not express the demands of decision maker, and it reduces the effectiveness and desirability of the model's results. Also in real conditions, various parameters and factors are uncertain with cause great complexity in decision maxing. So, for solving these probable, intuitionistic fuzzy multi-objective optimization problems have been proposed. Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Pareto-optimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfaction of an intuitionistic fuzzy decision. in this thesis first, addresses intuitionistic fuzzy multi-objective linear programming problems using triangular intuitionistic fuzzy numbers with mixed constraints. We convert the problem into single objective fuzzy goal programming problem. Then using different types of membership functions(linear and non-linear), we transform the problem in to crisp linear/nonlinear programming problem, which is solved by suitable crisp programming approaches. Next, The conflicting natures of the different objective have been handled by defining the membership functions corresponding to it in parabolic fuzzy set environment. A linear and non-linear membership functions corresponding to each objective has been taken in account.The objective is to present an algorithm for solving multi-objective optimization problem under the optimistic and pessimistic view point. finally, we have considered the imprecise coefficients of objective functions and constraints as intuitionistic fuzzy numbers and are approximated by its expected interval value. Further, a goal programming approach is applied to solve such problems.