Algorithm for modified knapsack problem
Let $X_1,\dots,X_n$ denote the scores of the individual members of the team.  Then the score $Y$ of the team is given by

$$Y = X_1 + \dots + X_n - \min(X_1,\dots,X_n).$$

So, by linearity of expectation, the expected value of the score of the team will be

$$\mathbb{E}[Y] = \mathbb{E}[X_1] + \dots + \mathbb{E}[X_n] - \mathbb{E}[\min(X_1,\dots,X_n)].$$

This suggests a natural approach.  Given a candidate list of members of the team, calculate the expected team score $\mathbb{E}[Y]$ using this formula.  Try all possibilities for the list of team members, compute the expected team score for each, and pick the one that has the highest expected team score.

The biggest challenge is to evaluate the above formula for a particular list of team members.  It is easy to evaluate $\mathbb{E}[X_i]$, but evaluating $\mathbb{E}[\min(X_1,\dots,X_n)]$ where $X_1,\dots,X_n$ are independent Gaussian random variables is challenging.  This is an order statistic, and there are various techniques for approximating its expected value.  One is to numerically estimate a certain integral.  Another is to use the approximation $-\Phi^{-1}({n-0.375 \over n+0.25})$, where $\Phi$ is the cdf of the normal distribution.  See J.P. Royston, Expected Normal Order Statistics (Exact and Approximate), Journal of The Royal Statistical Society Series C (Applied Statistics), vol 31 no 2, pp.161-165.  There may be better algorithms; hopefully this gives you the technical terms to use for searching.