This paper deals with multi-period project PORTFOLIO SELECTION problem. In this problem, the available budget is invested onthe best PORTFOLIO of projects in each period such that the net profit is maximized. We also consider more realisticassumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model ispresented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each timeperiod. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedureis presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solvethis type of problems. The GA is enhanced by a new solution representation and well selected operators. It also ishybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposedalgorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization(PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation resultsshow the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, theproposed algorithm is wisely combined with PSO to improve the computing time considerably.