► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers. One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.