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This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.