This work presents a new approach to maximize financial market investment returns. It incorporates twoEvolutionary Algorithms (EAs) combined with fundamental and technical investment strategies. The firstEA (simple) maintains its evolutionary parameters static during evolution.  The second (self-adaptive)introduces the variation operators’ parameters’ values in the representation for them to evolve.   TheEA is responsible for optimizing the weight that financial ratios from the F-Score have on composingstatic/dynamic portfolios.  Furthermore, it is also responsible for defining the importance that selectedtechnical indicators have, for revealing the best timing for market positions placement.  A fundamentaland a technical case study was created employing companies from the Standard & Poor’s 500 (SP500).These were trained/tested in a sliding window scheme between 01/2012 and 12/2018. Results showedthat both case studies surpassed the SP500 returns, performing their best results using a self-adaptiveEA combined with a static portfolio and a sliding window of 2 years of train/test. The technical case studyshowed better results in bear markets since it predicted some market declines. Its best subtest achievedreturns on average 2.2x and in its best 3.5x higher than the benchmark.  Its Sharpe Ratio achieved,on average, 4.9x and in its best 9x higher results than the benchmark.  The fundamental case studydisplayed better performance in the bull market, achieving high market prices. Its best subtest achievedreturns on average 2.4x and in its best 3.2x higher than the benchmark.  Its Sharpe Ratio achieved, onaverage, 4.4x and in its best 6.5x higher results than the benchmark.