Dynamic optimization is the problem of finding the minimum of a cost function subject to a constraint comprised of a system of differential equations. There are many algorithms to numerically solve such optimization problems. One such algorithm is multiple shooting. This paper reports an implementation of a multiple shooting algorithm in Python. The implementation is based on the open source platform JModelica.org, the integrator SUNDIALS and the optimization algorithm scipy_slsqp. The JModelica.org platform supports model descriptions encoded in the Modelica language and optimization specifications expressed in the extension Optimica. The Modelica/Optimica combination provides simple means to express complex optimization problems in a compact and user-oriented manner. The JModelica.org platform, in turn translates the high-level descriptions into efficient C code which can compiled and linked with Python. As a result, the numerical packages available for Python can be used to develop custom applications based on Modelica/Optimica specifications. An example is provided to illustrate the capabilities of the method.