This paper presents a multi-objective genetic programming based financial forecasting system, MOFGP. MOFGP is built upon our previous decision-making tool, FGP (Financial Genetic Programming) -. By taking advantage of the techniques of multi-objective evolutionary algorithms (MOEAs), MOFGP enhances FGP in a number of ways. Firstly, MOFGP is faster in obtaining the same quantity of diverse forecasting models optimized with respect to multiple conflicting objectives. This is attributed to the inherent property of MOEAs, i.e., a set of Pareto front solutions can be obtained in a single execution of its algorithm. Secondly, MOFGP is friendlier and simpler from the user's perspective. It is friendlier because it eliminates a number of user-supplied parameters previously required by FGP. Consequently, it becomes simpler as the user no longer needs to have a priori domain knowledge required for the proper use of those parameters. Finally, compared with FGP, which exploits a canonical single-objective approach to tackle a multi-criterion financial forecasting problem, MOFGP demonstrates the above advantages without seriously sacrificing its forecasting performance, although it suffers from an inadequate generalization capability over the test data in this study. Given its strengths and weaknesses, MOFGP could be employed as a useful starting investigative tool for financial decision making.