Excel Solver - Solver has converged to the current solution. All constraints are satisfied (1).

This means that Solver has found a series of “best” solutions that satisfy the constraints, and that have very similar objective function values; however, no single solution strictly satisfies Solver’s test for optimality.  The exact meaning depends on whether you are solving a smooth nonlinear problem with the GRG Nonlinear Solving method, or a non-smooth problem with the Evolutionary method. This message doesn’t appear for the Simplex LP Solving method.

When the GRG method is being used, this message means that the objective function value is changing very slowly as Solver progresses from point to point. More precisely, Solver stops if the absolute value of the relative change in the objective function, in the last few iterations, is less than the Convergence tolerance in the Solver Options dialog.  A poorly scaled model is more likely to trigger this stopping condition, even if the Use Automatic Scaling check box in the Solver Options dialog is selected.  If you are sure that your model is well scaled, you should consider why it is that the objective function is changing so slowly.  For more information, see the discussion of GRG Nonlinear Solving Method Stopping Conditions.

When the Evolutionary Solving method is being used, this message means that the “fitness” of members of the current population of candidate solutions is changing very slowly.  More precisely, the Evolutionary Solver stops if 99% or more of the members of the population have fitness values whose relative difference is less than the Convergence tolerance in the Solver Options dialog.  The fitness values incorporate both the objective function and a penalty for infeasibility, but since Solver has found some feasible solutions, this test is heavily weighted towards the objective function values.  If you believe that Solver is stopping prematurely when this test is satisfied, you can make the Convergence tolerance smaller, but you may also want to increase the Mutation Rate and/or the Population Size, in order to increase the diversity of the population of trial solutions.  For more information, see the discussion of Evolutionary Solving Method Stopping Conditions.