To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(x\) as the amount invested in the trust,
- \(y\) as the amount invested in the savings account.

The total investment is $60,000, so we have:
\[x + y = 60,000\]

We want to maximize the return on investment. The return from the trust is 2% of \(x\), and the return from the savings account is 3% of \(y\). Thus, the total return \(R\) can be represented as:
\[R = 0.02x + 0.03y\]

We also have constraints based on the advice given to the client:
1. At least 15% of the investment should be placed in the trust:
\[x \geq 0.15 \times 60,000\]
\[x \geq 9,000\]

2. At most 80% of the investment should be placed in the savings account:
\[y \leq 0.80 \times 60,000\]
\[y \leq 48,000\]

Additionally, \(x\) and \(y\) must be non-negative since they represent amounts of money.

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Investment_Optimization")

# Define the decision variables
x = m.addVar(lb=9000, name="trust_investment")  # Lower bound to ensure at least 15% investment
y = m.addVar(lb=0, ub=48000, name="savings_account_investment")  # Upper bound for at most 80%

# Objective function: Maximize return on investment
m.setObjective(0.02*x + 0.03*y, GRB.MAXIMIZE)

# Constraint: Total investment is $60,000
m.addConstr(x + y == 60000, "total_investment")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Trust Investment: ${x.x:.2f}")
    print(f"Savings Account Investment: ${y.x:.2f}")
    print(f"Total Return: ${0.02*x.x + 0.03*y.x:.2f}")
else:
    print("No optimal solution found")
```