The independent component analysis (ICA) with a single quadratic constraint on each source signal or column of the mixing matrix is extended to the case of multiple quadratic constraints. The criterion of Joint Approximate Diagonalization of Eigen-matrices (JADE) is used to measure the statistical independence. A new algorithm is derived to maximize the JADE criterion subject to the multiple quadratic constraints, using the augmented Lagrangian method. The extension offers the freedom to design various combinations of quadratic constraints. Examples include simultaneously constraining a source signal and the corresponding column of the mixing matrix, and two-sided constraints on the source signals or columns of the mixing matrix. Example results are provided to demonstrate the effectiveness of the algorithm.