I am using Pyomo to model my optimization problem (MILP) and solve it using Gurobi.
What would be the best, fastest or easiest way to find a heuristic solution using the Pyomo model, knowing that I do not care about the Gap bounds.
Note: I know that Gurobi has a heuristic solver but it doesn't tell what heuristic algorithm they are using!
Finding a heuristic solution to some MILP problem is complexity-wise as hard as optimizing it!
There is no best, fastest, easiest way in general. You always want to exploit some problem-characteristics.
As start, just use any MIP-solver and tune the params to reflect your needs. If you want just any heuristic solution, tune the solver for feasibility, probably meaning a higher frequency of heuristic-steps and early-stop with the first feasible solution.
Yes, you won't know what's Gurobi using internally. But knowing all of the code would not help much either. It's surely not something which you can find on wikipedia then (except for classic stuff like the feasibility pump or Relaxation induced neighborhood search).
If you want to know more about these methods, check out papers on MIP-heuristics in general! You will see, that most Heuristics are tightly coupled with the MIP-nature of the problem (although i expect some SAT-solver-usage internally too in commercial ones).