Minimize function with SciPy

[PierreLCV]

Hello everyone,
I'm currently trying to minimize a function using a Python algorithm. The function is a bit complicated and requires an FEA solver to be calculated. Anyway, I was able to define my function in the classical way according to the parameter I'm trying to optimize:

def fonction_calcul(Parametre1):
      blabla function definition
      return function value

As this last function definition works well, I can easily get the values of my calculation_function for the parameter values I want. Knowing this, I thought I could then "foolishly" use scipy.optimize to find the minimization value of my function, but alas, it doesn't work!
Here's what I get starting with x=1

x_start=1.0
>>>	
result = spo.minimize(fonction_calcul, x_start)
>>>	
result
      fun: 106.69134802297765
 hess_inv: array([[1]])
      jac: array([0.])
  message: 'Optimization terminated successfully.'
     nfev: 2
      nit: 0
     njev: 1
   status: 0
  success: True
        x: array([1.])

Overall, as you can see, minimize calculates a single value and stops at this value, considering that it has optimized well (knowing that in this case, the minimization value is around 0.03...).

I've tried minimize with a "classic" function (polynomial) and it works well for finding the minimization value...

Any idea how to make minimize function in my case?

Thanks a lot!
Pierre