I agree with the previous answers, and I also add the following.
We usually dont care about the time complexity of a genetic algorithm. We usually care how good are the results in comparison to some benchmark, and about the convergence rate.
You can see however that genetic algorithms runs in iterations. Initially, a set of solutions $S$ are generated randomly ($S$ is called a population). The costs of the solutions of $S$ are computed. Some operations are done over the the solutions of $S$ in each iterations such as crossover, mutation etc ... . The best $k$ solutions in $k$ are kept in $S$ and we continue as previous. After the last iteration, we output the best solution we found.
You can note here that the time cost of an iteration depends on the inner operations (e.g. crossovers, mutation and others, finding best $k$ distinct solutions, generate random solutions, calculate cost of the solutions of $S$ etc .. ) which are usually simple to implement, and also problem-dependent. In general, they depend on the size of a solution.
The execution time of a genetic algorithm also depends on the number of iterations (obviously !). Typically, we want to stop when we converge to a solution that is hardly improved. How to find the number of iterations that guarantee this ? there are some probabilistic analyses to find the average convergence time. See for example  (quite interesting results). But you will note that we did not reach yet the level of analyzing the complex problems where genetic algorithms are used. Therefore, in many cases, the number of iterations in a genetic algorithm is decided experimentally.
 Oliveto, Pietro S., Jun He, and Xin Yao. "Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results." International Journal of Automation and Computing 4.3 (2007): 281-293.