Well, depending on what it means to optimize, I suspect that you can. I mean, are you trying to maximize a final value of one of the variables, or minimize the sum of squared deviations of the solution of the ODE to a given function, or do you mean something else when you say optimize?
In general, once you have made explicit your objective function that you are trying to minimize, it is not important that it comes from the numerical solution of an ODE, it is just important that you understand how to go from (parameters you are able to change) to (value of objective function). Once you have that mapping, you can call an optimization funtion such as fmincon or lsqcurvefit. See, for example, Fit an ODE or Optimize an ODE in Parallel.
Alan Weiss
MATLAB mathematical toolbox documentation
