Greedy Algorithm: Optimal Substructure
Greedy algorithms are often used when solving optimization problems, like finding the maximum or the minimum of a certain quantity, under certain conditions. Solutions that satisfy those extrema are called optimal solutions.
To answer your question, let's look at a simple example, change-making problem:

Given a set of integer values of coins $C = \{c_1, …, c_n\}$ and an amount $W$, what is the minimum number of coins to add-up to the amount $W$?

The classical greedy approach is the following:
While W &gt; 0
   pick the largest coin c that is &lt;= W
   W &lt;- W - c

For example, with $C = \{1, 2, 5\}$ and $W = 13$, you will pick $5$, $5$, $2$ and $1$, and you can show that the minimum number of coins required is indeed $4$.
However, this algorithm does not always provide an optimal solution. For example, if $C = \{1, 4, 5\}$ and $W = 8$, the greedy algorithm will choose coins $5, 1, 1, 1$ when there is a solution $4, 4$ using less coins.
Back to the topic of your question, the change-making problem does not have an optimal substructure, unless you add some hypotheses on $C$