Cher Yves, I took at look at your notebook. I am uncertain what you are asking, but I think the question is why the built-in function NMinimize is not parallelized. Is that correct?

I agree is would be nice if some of the numerically intensive functions in Mathematica were designed to take an option such as Parallelize->True, but it doesn't exist (it is one of the suggestions here: http://community.wolfram.com/groups/-/m/t/181759?ppauth=bCSNFi3t).

However, in your example you have this construct:

es0 = NMinimize[{Total[
    ParallelTable[(mmoyen0[datamhtcor[[j, 1]], datamhtcor[[j, 2]], 
         datamhtcor[[j, 3]], dist, ee, gx, gy, gz] - 
        datamhtcor[[j, 4]])^2, {j, 1, Length[datamhtcor]}]], 
   gx > 2. && gy > 2. && gz > 2 && ee < Abs[dist/3]}, {{gx, 2, 
    2.2}, {gy, 2.1, 2.2}, {gz, 2.1, 
    2.2}, {dist, -5, -2}, {ee, .3, .5}} , Method -> "NelderMead", 
  MaxIterations -> 100]

If NMinimize were parallelized, then this isn't likely to work because you would be wrapping on parallelization inside another. You would be better off precomputing (e.g,)

minimizationTarget =Total[ParallelTable[(mmoyen0[datamhtcor[[j, 1]], datamhtcor[[j, 2]], 
      datamhtcor[[j, 3]], dist, ee, gx, gy, gz] - 
     datamhtcor[[j, 4]])^2, {j, 1, Length[datamhtcor]}]]

and then minimizing that in a subsequent step:

NMinimize[{minimizationTarget,, gx > 2. && gy > 2. && gz > 2 && ee < Abs[dist/3]}, {{gx, 2, 
   2.2}, {gy, 2.1, 2.2}, {gz, 2.1, 
   2.2}, {dist, -5, -2}, {ee, .3, .5}} , Method -> "NelderMead", 
 MaxIterations -> 100]

One last observation, instead of doing this:

(*test ù ParallelTable est utilisé pour les 60 points expériementaux *)

    t1 = AbsoluteTime[];
    Do[Total[ParallelTable[(mmoyen0[datamhtcor[[j, 1]], 
           datamhtcor[[j, 2]], datamhtcor[[j, 3]], -10, 0., 2, 2, 2] - 
          datamhtcor[[j, 4]])^2, {j, 1, Length[datamhtcor]}]], {i, 1, 
      30}]
    t2 = AbsoluteTime[] - t1

There is a handy function Timing[] which you can wrap around the Do.