This paper aims to address a more realistic model of the portfolio optimization problem, unlike other previous evolutionary multiobjective optimization approaches. For this purpose, an order-based representation is proposed, which can be easily extended to handle various realistic constraints like floor and ceiling constraint and cardinality constraint. Furthermore, the current experimental platform for evolutionary multiobjective portfolio optimization will be improved by introducing diversity measures and statistical analysis that are commonly used in performance assessment of multiobjective optimizers. Comparative study with other conventional representations, based on benchmark problems obtained from the OR-library, demonstrated that the proposed representation is able to attain a better approximation of the efficient frontier in terms of proximity and diversity. Experimental results also validated its viability and practicality in handling the various realistic constraints. Lastly, preference based techniques are considered also, allowing the evolutionary search to be focused on specific region of the efficient frontier. Future work includes improving the algorithmic model with more sophisticated variation operators and local search operators for better exploration and exploitation of the search space.