R Package GA:
https://cran.r-project.org/web/packages/GA/vignettes/GA.html
As Eduardo suggested above, you can try R programming language which has several genetic algorithm packages.
R Package GA is more than enough for most of the fundemental needs. It is compatible with binary, real-valued, and permutation representations, and includes various cross-over and mutation operators. The only thing you need is customizing the code for your problem such as providing your objective function so that the fitness values of the solutions can be computed. When you get used to the package, you can define your own genetic operators.
There are also several other packages. For example, see, "mco" for Multiple Criteria Optimization Algorithms and Related Functions and see "nsga2R' for famous multi-objective evolutionary algorithm NSGA-II: Elitist Non-dominated Sorting Genetic Algorithm.
GA: https://cran.r-project.org/web/packages/GA/GA.pdf
mco: https://cran.r-project.org/web/packages/mco/mco.pdf
nsga2R: https://cran.r-project.org/web/packages/nsga2R/nsga2R.pdf
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