Hi, Recently there have been discussions about possible extensions of scipy.optimize.differential_evolution() : * https://github.com/scipy/scipy/issues/6878 is about having the callback function receive the function value * https://github.com/scipy/scipy/issues/6879 is about customizing of population initialization The differential_evolution function being already quite complex, we thought it would be better to expose a "lower level" API to control the iteration in the algorithm. This would be done by making the DifferentialEvolutionSolver class (in scipy/optimize/_differentialevolution.py) public and cleaning it a bit. I submitted a pull request for this : https://github.com/scipy/scipy/pull/6923 One noticeable thing is that it also makes the class a context manager to encapsulate the population initialization (in enter step) and final polishing (in exit step). Ultimately, usage of this class (renamed as DifferentialEvolution) would be: with DifferentialEvolution(func, bounds) as solver: # iterate until maxiter/maxfev is reached or the algorithm converged for step in solver: if mycallback(step.x, step.fun): break result = solver.result I might look a bit fancy, but I think it makes sense to regard a computation as a kind of context. Feedback welcome! Cheers, Denis.