This paper describes a new approach to portfolio management using stocks. The investment models
tested incorporate a fundamental and technical approach using financial ratios and technical indicators.
A Multi-Objective Evolutionary Algorithms (MOEA) with two objectives, the return and the risk, are used
to optimize the models. Three different chromosomes are used for representing different investment
models with real constraints equivalent to the ones faced by portfolio managers. To validate the present
solution three case studies are presented for the S&P 500 for the period June 2010 until 2014. Simulations
demonstrate that the stock selection based on financial ratios can be used to choose the best companies
in operational terms, obtaining returns above the market average with low variances in their returns. The
increase of fundamental indicators enhances the quality of the chromosomes found by the MOEA, and the
results of real simulations become more precise. Some of the best chromosomes found by the algorithms
invest in stocks with high return on equity (ROE), in conjunction with high rate of growth of the net
income and a high profit margin. To obtain stocks with high valuation potential, it is necessary to choose
companies with a lower or average market capitalization, low PER, high rates of revenue growth and high
operating leverage.