Crossover method for genetic algorithm I got an amount of numbers, they are shuffled and represent the individuals.
For example (3,1,3,2,5,22,5) is one individual or (22,3,1,3,5,5,2).

Mutation is done quite easy by permutation within an individual.

The problem is to find a crossover method between 2 individuals, in which the quantity of the values stay the same(for the above example 2 times 3, 2 times 5, 1 time 22 and so on). I searched a lot on it but only found crossover methods for either unordered / ordered lists. Those for ordered lists seemed promising but they require that every value only occur ones.

Do you know a method to solve this problem or got an idea for altering an crossover for ordered list method like PMX?
You can choose a random threshold $t \leq n$ such that the first $t$ of the first individual is taken and then you take the rest from the other individual, so in your case, if $t = 4$, your new individual is $(3,1,3,2,22,5,5)$.  As Richerby clarifies below, you take the first $t$ elements from the first individual and put the remaining $n-t$ elements in the order they appear in the second individual.

Making sure that you take the correct number is done by simply counting using a hashmap.  Below, correct_count is a map from an element in an individual to the number of times it occurs, ind1 and ind2 are the two individuals you're trying to crossover.

correct_count = {}
for x in ind1:
    correct_count{x} += 1

t = random(n)
new_ind = ind1[0:t]
count = {}
for a in new_ind:
    count{a} += 1
for x in ind2:
    if count{x} != correct_count{x}:
        new_ind.append(x)
        count{x} += 1