Normalization(minmax) gives me worse results than before in KNN, follow up actions?
Is bad luck.

Look what (I think) is happening:

When you use MinMaxScaler, what you do is relativize (reduce or augment) the distance between individuals in a way it was not before the change.

Let's suppose your best model is defined by the variable $X_j$ from the variable set $X=\{X_1,X_2,...,X_n\}$. When you change the set to an scalled one: $S=\{S_1,S_2,...,S_n\}$, what you do is giving the same importance to every variable, instead of preserving what the variables said before scalling.

$X_3$ gives better information on the output than $S_3$ (and in $S$, all are scalled), $S_3$ gives less information because is mixed with all other variables.

Imagine $X_3$ as something like "age" (and you are determining probability of having cancer), the greater the age, the greater the probability. This is true for $S_3$ also, but when $S_3$ is combined with the rest of $S$, $S_3$ looses importance amongst them. With $X_3$, age keeps its relative importance (bigger values amongst lesser values).

This does not happen very often, you could find yourself on the opposite situation: When combined $S$ might be a very powerful set.

That is why I think you were simply, unlucky.